| Geography | Month |
|---|---|
| 202306 | |
| % | |
| Canada | 0.6 |
| Newfoundland and Labrador | 2.0 |
| Prince Edward Island | 1.0 |
| Nova Scotia | 1.8 |
| New Brunswick | 2.3 |
| Quebec | 1.2 |
| Ontario | 1.1 |
| Manitoba | 1.2 |
| Saskatchewan | 2.6 |
| Alberta | 0.9 |
| British Columbia | 1.8 |
| Yukon Territory | 1.7 |
| Northwest Territories | 1.9 |
| Nunavut | 1.6 |
| Geography | Month | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202206 | 202207 | 202208 | 202209 | 202210 | 202211 | 202212 | 202301 | 202302 | 202303 | 202304 | 202305 | 202306 | |
| percentage | |||||||||||||
| Canada | 0.66 | 0.49 | 0.14 | 0.13 | 0.17 | 0.24 | 0.88 | 0.32 | 0.33 | 0.26 | 0.14 | 0.18 | 0.20 |
| Newfoundland and Labrador | 0.53 | 0.50 | 0.47 | 0.49 | 0.73 | 0.49 | 0.93 | 2.43 | 0.81 | 0.70 | 0.84 | 0.99 | 1.23 |
| Prince Edward Island | 15.97 | 9.23 | 5.27 | 3.04 | 8.45 | 8.22 | 3.45 | 10.49 | 14.17 | 8.25 | 7.86 | 2.25 | 3.07 |
| Nova Scotia | 1.79 | 3.37 | 0.43 | 0.40 | 0.37 | 0.43 | 16.87 | 0.83 | 0.91 | 0.72 | 0.58 | 0.70 | 0.72 |
| New Brunswick | 0.67 | 0.53 | 0.52 | 0.50 | 0.56 | 0.73 | 12.18 | 1.21 | 1.77 | 0.76 | 0.73 | 0.78 | 1.71 |
| Quebec | 1.55 | 0.97 | 0.18 | 0.28 | 0.26 | 0.19 | 1.73 | 0.67 | 0.95 | 0.77 | 0.33 | 0.53 | 0.59 |
| Ontario | 1.30 | 0.95 | 0.25 | 0.25 | 0.21 | 0.53 | 0.73 | 0.67 | 0.64 | 0.48 | 0.25 | 0.26 | 0.30 |
| Manitoba | 0.68 | 3.49 | 0.48 | 0.40 | 0.37 | 0.58 | 9.72 | 0.78 | 0.75 | 0.80 | 0.68 | 0.84 | 0.93 |
| Saskatchewan | 6.45 | 4.85 | 1.30 | 0.73 | 1.31 | 1.44 | 7.51 | 0.62 | 0.89 | 0.51 | 0.55 | 0.76 | 0.93 |
| Alberta | 1.45 | 0.91 | 0.39 | 0.30 | 0.33 | 0.38 | 1.56 | 0.40 | 0.44 | 0.36 | 0.33 | 0.37 | 0.48 |
| British Columbia | 0.64 | 0.91 | 0.28 | 0.21 | 0.66 | 0.33 | 2.77 | 0.44 | 0.44 | 0.38 | 0.27 | 0.36 | 0.38 |
| Yukon Territory | 3.32 | 2.54 | 2.09 | 2.07 | 2.34 | 2.20 | 2.50 | 41.12 | 2.70 | 30.75 | 2.48 | 8.17 | 4.07 |
| Northwest Territories | 3.20 | 2.74 | 2.38 | 2.05 | 2.00 | 2.09 | 2.56 | 6.03 | 2.47 | 38.31 | 3.64 | 10.11 | 3.58 |
| Nunavut | 1.55 | 1.52 | 1.30 | 2.35 | 2.85 | 101.77 | 43.21 | 2.83 | 2.61 | 2.50 | 2.47 | 23.47 | 2.74 |
By AI Lab, Canadian Food Inspection Agency
The AI Lab team at the Canadian Food Inspection Agency CFIA) is composed of a diverse group of experts, including data scientists, software developers, and graduate researchers, all working together to provide innovative solutions for the advancement of Canadian society. By collaborating with members from inter-departmental branches of government, the AI Lab leverages state-of-the-art machine learning algorithms to provide data-driven solutions to real-world problems and drive positive change.
At the CFIA's AI Lab, we harness the full potential of deep learning models. Our dedicated team of Data Scientists leverage the power of this transformative technology and develop customised solutions tailored to meet the specific needs of our clients.
In this article, we motivate the need for computer vision models for the automatic classification of seed species. We demonstrate how our custom models have achieved promising results using "real-world" seed images and describe our future directions for deploying a user-friendly SeedID application.
At the CFIA AI Lab, we strive not only to push the frontiers of science by leveraging cutting-edge models but also in rendering these services accessible to others and foster knowledge sharing, for the continuous advancement of our Canadian society.
To understand how image classification models work, we first define what exactly computer vision tasks aim to address.
Computer Vision models are fundamentally trying to solve what is mathematically referred to as ill-posed problems. They seek to answer the question: what gave rise to the image?
As humans, we do this naturally. When photons enter our eyes, our brain is able to process the different patterns of light enabling us to infer the physical world in front of us. In the context of computer vision, we are trying to replicate our innate human ability of visual perception through mathematical algorithms. Successful computer vision models could then be used to address questions related to:
Yet, while these appear to be simple tasks, computers still have difficulties in accurately interpreting and understanding our complex world.
To understand why computers seemingly struggle to perform these tasks, we must first consider what an image is.
Are you able to describe what this image is from these values?
This image shows a brown and white pixelated image of a person’s face. The person's face is pixelated, with the pixels being white and the background being brown. Next to the image, there's a zoomed in image showing the pixel values corresponding to a small patch of the original image.
An image is a set of numbers, with typically three colour channels: Red, Green, Blue. In order to derive any meaning from these values, the computer must perform what is known as image reconstruction. In its most simplified form, we can mathematically express this idea through an inverse function:
x = F-1(y)
Where:
y represents data measurements (ie. pixel values).
x represents a reconstructed version of measurements, y, into an image.
However, it turns out solving this inverse problem is harder than expected due to its ill-posed nature.
When an image is registered, there is an inherent loss of information as the 3D world gets projected onto a 2D plane. Even for us, collapsing the spatial information we get from the physical world can make it difficult to discern what we are looking at through photos.
Michelangelo (1475-1564). Occlusion caused by different viewpoints can make it difficult to recognise the same person.
The image shows three paintings of different figures, each with a different expression on their faces. One figure appears to be in deep thought, while the other two appear to be in a state of contemplation. The paintings are made of a dark, rough material, and the details of their faces are well-defined. The overall effect of the image is one of depth and complexity. The paintings are rotated in each frame to create a sense of change.
Bottom of soda cans. Different orientations can make it impossible to identify what is contained in the can.
The image shows five metal cans, four of them with a different patch of color on the lid. The colors are blue, green, red, and yellow. The cans are arranged on a countertop. The countertop is made of a dark surface, such as granite or concrete.
Yale Database of faces. Variations in lighting can make it difficult to recognise the same person (recall: all computers “see” are pixel values).
The image shows two images of the same face. The images are captured from different angles, resulting in two different perceived expressions of the face. On the left frame the man a neutral facial expression, whereas on the right frame he has a serious and angry expression.
Rick Scuteri-USA TODAY Sports. Different scales can make it difficult to understand context from images.
The image shows four different images, at different scales. The first images contain only what looks like the eye of a bird. The second image contains the head and neck of a goose. The third image shows the entire animal, and the fourth image shows a man standing in front of the bird pointing in a direction.
Different photos of chairs. Intra-class variation can make it difficult to categorise objects (we can discern a chair through its functional aspect)
The image shows 5 different chairs. The first one is a red chair with a wooden frame. The second one is a black leather swivel chair. The third looks like an unconventional artistic chair. The fourth one looks like a minimalist office chair, and the last one looks like a bench.
It can be difficult to recognise objects in 2D pictures due to possible ill-posed properties, such as:
While computer vision tasks may, at first glance, appear superficial, the underlying problem they are trying to address is quite challenging!
Now we will address some Deep Learning driven solutions to tackle computer vision problems.
Graphical representation of a convolutional neural network (CNN) architecture for image recognition. (Hoeser and Kuenzer, 2020)
This is a diagram of a convolutional neural network (ConvNet) architecture. The network consists of several layers, including an input layer, a convolutional layer, a pooling layer, and an output layer. The input layer takes in an image and passes it through the convolutional layer, which applies a set of filters to the image to extract features. The pooling layer reduces the size of the image by applying a pooling operation to the output of the convolutional layer. The output layer processes the image and produces a final output. The network is trained using a dataset of images and their corresponding labels.
Convolutional Neural Networks (CNNs) are a type of algorithm that has been really successful in solving many computer vision problems, as previously described. In order to classify or identify objects in images, a CNN model first learns to recognize simple features in the images, such as edges, corners, and textures. It does this by applying different filters to the image. These filters help the network focus on specific patterns. As the model learns, it starts recognizing more complex features and combines the simple features it learned in the previous step to create more abstract and meaningful representations. Finally, the CNN takes the learned features and to classify images based on the classes it's been trained with.
Evolution of CNN architectures and their accuracy, for image recognition tasks from 2012 to 2019. (Hoeser and Kuenzer, 2020).
The image shows the plot of the size of different CNN architectures and models from the year 2012 until 2019. Each neural network is depicted as a circle, with the size of the circle corresponding to the size of the neural network in terms of number of parameters.
The first CNN was first proposed by Yann LeCun in 1989 (LeCun, 1989) for the recognition of handwritten digits. Since then, CNNs have evolved significantly over the years, driven by advancements in both model architecture and available computing power. To this day, CNNs continue to prove themselves are powerful architectures for various recognition and data analysis tasks.
Vision Transformers (ViTs) are a recent development in the field of computer vision that apply the concept of transformers, originally designed for natural language processing tasks, to visual data. Instead of treating an image as a 2D object, Vision Transformers view an image as a sequence of patches, similar to how transformers treat a sentence as a sequence of words.
An overview of a ViT as illustrated in An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. Since the publication of the original ViT, numerous variations and flavours have been proposed and studied.
The image shows the diagram of the ViT architecture. There is an image of the input image, being splitted into different patches, and each patch is fed into the neural network. The network consists of a transformer encoder block and an MLP Head block, followed by a classification head.
The process starts by splitting an image into a grid of patches. Each patch is then flattened into a sequence of pixel vectors. Positional encodings are added to retain the positional information, as is done in transformers for language tasks. The transformed input is then processed through multiple layers of transformer encoders to create a model capable of understanding complex visual data.
Just as Convolutional Neural Networks (CNNs) learn to identify patterns and features in an image through convolutional layers, Vision Transformers identify patterns by focusing on the relationships between patches in an image. They essentially learn to weigh the importance of different patches in relation to others to make accurate classifications. The ViT model was first introduced by Google's Brain team in a paper in 2020. While CNNs dominated the field of computer vision for years, the introduction of Vision Transformers demonstrated that methods developed for natural language processing could also be used for image classification tasks, often with superior results.
One significant advantage of Vision Transformers is that, unlike CNNs, they do not have a built-in assumption of spatial locality and shift invariance. This means they are better suited for tasks where global understanding of an image is required, or where small shifts can drastically change the meaning of an image.
However, ViTs typically require a larger amount of data and compute resources compared to CNNs. This factor has led to a trend of hybrid models that combine both CNNs and transformers to harness the strengths of both architectures.
Canada's multi-billion seed and grain industry has established a global reputation in the production, processing, and exportation of premium-grade seeds for planting or grains for food across a diverse range of crops. Its success is achieved through Canada's commitment to innovation and the development of advanced technologies, allowing for the delivery of high-quality products with national and international standards with diagnostic certification that meet both international and domestic needs.
Naturally, a collaboration between a research group from the Seed Science and Technology Section and the AI Lab of CFIA was formed to maintain Canada's role as a reputable leader in the global seed or grain and their associated testing industries.
The seed quality of a crop is reflected in a grading report, whereby the final grade reflects how well a seed lot conforms with Canada's Seeds Regulations to meet minimum quality standards. Factors used to determine crop quality include contaminated weed seeds according to Canada's Weed Seeds Order, purity analysis, and germination and disease. While germination provides potentials of field performance, assessing content of physical purity is essential in ensuring that the crop contains a high amount of the desired seeds and is free from contaminants, such as prohibited and regulated species, other crop seeds, or other weed seeds. Seed inspection plays an important role in preventing the spread of prohibited and regulated species listed in the Canadian Weed Seeds Order. Canada is one of the biggest production bases for global food supply, exporting huge number of grains such as wheat, canola, lentils, and flax. To meet the Phyto certification requirement and be able to access wide foreign markets, analyzing regulated weed seeds for importing destinations is in high demand with quick turnaround time and frequent changes. Testing capacity for weeds seeds requires the support of advanced technologies since the traditional methods are facing a great challenge under the demands.
Presently, the evaluation of a crop's quality is done manually by human experts. However, this process is tedious and time consuming. At the AI Lab, we leverage advanced computer vision models to automatically classify seed species from images, rendering this process more efficient and reliable.
This project aims to develop and deploy a powerful computer vision pipeline for seed species classification. By automating this classification process, we are able to streamline and accelerate the assessment of crop quality. We develop upon advanced algorithms and deep learning techniques, while ensuring an unbiased and efficient evaluation of crop quality, paving the way for improved agricultural practices.
In this project, we employ a custom computer vision model to assess content purity, by identifying and classifying desired seed species from undesired seed species.
We successfully recover and identify the contamination by three different weed species in a screening mixture of wheat samples.
Our model is customised to accept unique high resolution, 19-channel multi-spectral image inputs and achieves greater than 95% accuracy on held out testing data.
We further explored our model's potential to classify new species, by injecting five new canola species into the dataset and observing similar results. These encouraging findings highlight our model's potential for continual use even as new seed species are introduced.
Our model was trained to classify the following species:
Our model was able to correctly identify each seed species with an accuracy of over 95%.
Moreover, when the three thistle seeds were integrated with the wheat screening, the model achieved an average accuracy of 99.64% across 360 seeds. This demonstrated the model's robustness and ability to classify new images.
Finally, we introduced five new canola species and types and evaluated our model's performance. Preliminary results from this experiment showed a ~93% accuracy on the testing data.
In this project, we employ a 2-step process to identify a total of 15 different seed species with regulatory significance and morphological challenge across varying magnification levels.
First, a seed segmentation model is used to identify each instance of a seed in the image. Then, a classification model classifies each seed species instance.
We perform multiple ablation studies by training on one magnification profile then testing on seeds coming from a different magnification set. We show promising preliminary results of over 90% accuracy across magnification levels.
Three different magnification levels were provided for the following 15 species:
A mix of 15 different species were taken at varying magnification levels. The magnification level was denoted by the total number of instances of seeds present in the image, either: 1, 2, 6, 8, or 15 seeds per image.
In order to establish a standardised image registration protocol, we independently trained separate models from a subset of data at each magnification then evaluated the model performance across a reserved test set for all magnification levels.
Preliminary results demonstrated the model's ability to correctly identify seed species across magnifications with over 90% accuracy.
This revealed the model's potential to accurately classify previously unseen data at varying magnification levels.
Throughout our experiments, we tried and tested out different methodologies and models.
Advanced models equipped with a canonical form such as Swin Transformers fared much better and proved to be less perturbed by the magnification and zoom level.
Automatic seed classification is a challenging task. Training a machine learning model to classify seeds poses several challenges due to the inherent heterogeneity within and between different species. Consequently, large datasets are required to effectively train a model to learn species-specific features. Additionally, the high degree of similarity among different species within genera for some of them makes it challenging for even human experts to differentiate between closely related intra-genus species. Furthermore, the quality of image acquisition can also impact the performance of seed classification models, as low-quality images can result in the loss of important information necessary for accurate classification.
To address these challenges and improve model robustness, data augmentation techniques were performed as part of the preprocessing steps. Affine transformations, such as scaling and translating images, were used to increase the sample size, while adding Gaussian noise can increase variation and improve generalization on unseen data, preventing overfitting on the training data.
Selecting the appropriate model architecture was crucial in achieving the desired outcome. A model may fail to produce accurate results if end users do not adhere to a standardized protocol, particularly when given data that falls outside the expected distribution. Therefore, it was imperative to consider various data sources and utilize a model that can effectively generalize across domains to ensure accurate seed classification.
The seed classification project is an example of the successful and ongoing collaboration between the AI Lab and the Seed Science group at the CFIA. By pooling their respective knowledge and expertise, both teams contribute to the advancement of Canada's seed and grain industries. The seed classification project showcases how leveraging advanced machine learning tools has the potential to significantly enhance the accuracy and efficiency of evaluating seed or grain quality with compliance of Seed or Plant Protection regulations, ultimately benefiting both the agricultural industry, consumers, Canadian biosecurity, and food safety.
As Data Scientists, we recognise the importance of open-source collaboration, and we are committed to upholding the principles of open science. Our objective is to promote transparency and engagement through open sharing with the public.
By making our application available, we invite fellow researchers, seed experts, and developers to contribute to its further improvement and customisation. This collaborative approach fosters innovation, allowing the community to collectively enhance the capabilities of the SeedID application and address specific domain requirements.
If you have any questions about my article or would like to discuss this further, I invite you to Meet the Data Scientist, an event where authors meet the readers, present their topic and discuss their findings.
Register for the Meet the Data Scientist event. We hope to see you there!
MS Teams – link will be provided to the registrants by email
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Release date: August 21, 2023
Catalogue number: 45200003
ISSN: 2816-2250
Listen to "Eh Sayers" on:
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Ladies, Gentlemen, and Gentlethem!
While every census is special, the 2021 Census was historic. It was the first to include a question about gender, making Canada the first country to collect and publish data on gender diversity from a national census.
In this episode, we explore gender with drag king Cyril Cinder and we talk Census 2021 with StatCan’s Anne Milan.
Join us for a new kind of gender reveal.
Tegan Bridge
Cyril Cinder, Anne Milan
Cyril: Molly knew from experience that dresses were trouble. Dresses have tight places and zippers you can’t reach. Dresses mean troublesome tights and fancy shoes with no purpose. Dresses with no pockets mean nowhere to put interesting rocks, and nowhere to keep dog treats in case you find a stray. Dresses were not right on a regular day and they were definitely not right for something as important as picture day!
Tegan: (Stage whisper) Welcome to Eh Sayers a podcast from statistics, Canada, where we meet the people behind the data and explore the stories behind the numbers. I'm your host, Tegan Bridge, and I'm whispering because we're listening to drag story time. Shh!.
Cyril: Molly wanted to look like she was going on an adventure, not like she was going to a tea party. But she had an idea to save picture day. Her brother’s old tuxedo! It was perfect.
Dashing. Comfortable. Plenty of pockets. She had tried it on once when he was at chess club. It fit just right then, and Molly was sure it would look just as great today!
Tegan: You just heard part of Molly’s Tuxedo by Vicki Johnson, in which Molly has to decide whether to wear the dress her mom picked out for her school picture day or the tuxedo she wants to wear. It’s a book which explores gender in a kid-friendly way. You might hear Molly’s Tuxedo read at a drag story time, and if you’re in the Ottawa area, you might just hear it read by Cyril Cinder.
Cyril: My name is Cyril Cinder, and I'm an Ottawa based drag performer and drag king who has been performing since 2014.
Tegan: What's a drag king?
Cyril: Drag kings are drag artists who present or perform a masculine persona as part of their drag performance. That might involve parody, exploration or expansion of masculine gender norms and performance types. Um, they can be, you know, suave, they can be comedic, they can be big and extravagant and super over the top. Drag kings can be absolutely anyone and everything.
Tegan: Where does the name Cyril Cinder come from?
Cyril: Uh, I named myself in drag. That's not always a thing that happens. Sometimes you are given a name, but in the Canadian scene, usually we pick our own names, and I kind of wanted to sound like the alter ego of a super villain. So, and alliteration sounds cool. So I went with Cyril Cinder. I didn't wanna choose a pun name because I didn't trust myself to be clever enough to come up with something that nobody else would come up with. And the great benefit of it has been that it's also appropriate for all ages. It just sounds cool.
Tegan: I love it. What kind of performances do you do?
Cyril: I tend to lean into the age-old tradition of drag, which is the lip sync performance. I perform a lot in bars or in very different venues, music venues. I'm also a story time performer, so I will entertain and read books to children and audiences and families of all ages. I'm also a speaker and travel to conferences to talk about what I do as a drag performer, the increase attacks against the 2SLGBTQIA+ community in Canada, and how that does tend to specifically target drag performers, as well as mental health because I also work as a registered psychotherapist.
Tegan: We mentioned drag story times at the top of the show. Why are drag story times important?
Cyril: For me, drag story time is a couple of important things. I mean, right off the bat, it's a pro-literacy initiative. Whenever you're doing something to make reading look a little bit more fun, you are encouraging children to be more interested in reading, to be more excited about books, and literacy is such an important foundation of our society. But the other part is that it's also an anti-bullying initiative, right? Whether or not we are, you know, exposing kids to a positive queer role model for the first time, or to someone who's maybe a bit more gender nonconforming, doesn't quite match the boys and girls archetypes and binaries that they're often exposed to at home and at school and in media. It's an opportunity for them to see that and see that that's not that strange actually, that that's okay, that there's nothing too weird about being fabulous and sparkly and excited and fun. And if we can introduce children to positive role models of all different kinds of diversity at a young age, as they get older, that becomes less and less of a axis of difference for them, something for them to isolate or pick out about their peers and say, oh, you're not like me in this way. It becomes something that they can say, oh yeah, there are people like this. This is normal. This is okay.
Tegan: You mentioned binaries and archetypes for little boys and little girls. Could you say more about that? What's the gender binary and why is it a problem?
Cyril: So the gender binary is the idea that there are only two genders. Gender is distinct from sex.
Sex is a series of biological characteristics. Sex is also not an actual binary between male and female, and we don't only see this non-binary nature of sex represented in the human species, but in multiple different species. So that is just to say that there are many, many different biological expressions that don't fit within the male-female solid archetype.
Gender is a different thing than sex, though. Gender is an experience of one's gender, role, and identity within society: the concept of being masculine, feminine, androgynous, the idea of rather than being male or female, of being man or woman. And as much as the archetypes and binary roles of male and female don't capture the full breadth of the experience of sex, the binary of woman and man also doesn't capture the full breadth of experience that humans can have of our gender and of our gender identities.
The gender binary can be used to control people. It can be used to force people into things that they don't want for themselves, right? We can see very strong, uh, expectations on men, for example, on what kind of emotions they're allowed to show, what kind of careers they're allowed to pursue, how they're supposed to feel about caretaking or sex or power or any of these things. And these are equally damaging to people of all genders. You know, women get told not to be too bossy, uh, that they're over emotional, that they can't be trusted to make decisions or be in positions of leadership, right? These sorts of boxes that we force people into. And people who don't feel like they fit into either of the binary gender options, male or female, people who fall under the very large umbrella of the non-binary spectrum really also deserve to have their experience of gender understood, respected, and validated.
Tegan: What are your preferred pronouns?
Cyril: Yeah, so I actually identify as non-binary out of drag, but my drag character, Cyril Cinder, is a man that is the gender identity of that character. So in drag, I exclusively use he/ him pronouns, but out of drag I use she/he/they pronouns. Any pronouns really I'm comfortable with. But whenever I'm referring to Cyril Cinder, the drag character, I always prefer to use he/him.
Tegan: Is it a challenge living as an out non-binary person?
Cyril: It can be. I think I certainly don't experience certain challenges that other people who might hold the same identity of non-binary as, as me actually experience, um, because of my flexibility with pronouns, with the she/he/they. I am comfortable if someone, you know, defaults to she looking at me because I was assigned female at birth and I, you know, when I'm not in drag, do have a somewhat feminine gender presentation and someone looks at me and goes, ah, a woman. It's not correct, but it's also not like the worst thing in the world for me.
At the same time, other people for whom that would feel really actually quite distressing and upsetting and invalidating for them. I think they face a greater challenge, right? Someone who exclusively uses they/them pronouns who might constantly have to correct people who would default to he or she or might overtly refuse to use their pronouns because of a belief that they have and a subsequent then desire to invalidate that person.
And that's really hard because then you're moving through the world and you're trying to tell people who you are. We have this innate human desire to be seen by the people around us. We are a social animal. We exist in a society. We don't do well independently on our own. We're not built for that. So when you're trying to say to someone like, “Hey, this is who I am!” And they go, “No, you're not. I know you better than you do, and in fact, this thing that you're doing, it's really a problem. It's really dangerous. It's really bad actually, and you should feel shame for that.” That's not an experience anyone seeks to have when they're trying to order a coffee or talk to their boss or just go about their day and go about their life.
Tegan: You're a drag king. You're a performer, and I think it goes without saying that drag is a performance. In what ways is all gender a performance?
Cyril: So gender is performance. It is something that we put on whether you are choosing to wear a dress or a three-piece suit or both at the same time. You were saying something about what feels right to you about who you are, about, you know, just different little elements of yourself that we can express in little ways, and that might be through comparison, through contrast, through exaggeration, through celebration. We're all doing little things and all of those expressions are a completely valid way of looking at everyone's individual experience of gender.
We have gender identity, we have gender expression. Those are also two different things. How someone identifies in their gender might be different than how they express it. I'm non-binary. And so that's my gender identity, but I have a very feminine gender expression. I also can exhibit a very masculine gender expression. That's quite fluid for me. That moves around a lot. Not everybody might have the same experience, but it's, it's important to be able to articulate these different parts of it because we're only just enriching our understanding of the human experience.
Tegan: A good friend of mine whenever he needs someone who looks at the world in a very different way than he does. He pauses and gets a thoughtful look on his face, and he says, “Life is a rich tapestry!” The human experience and the diversity of that experience certainly is a rich tapestry. Gender identity, especially, is fascinating. But it's not something that we here at stat can have measured. Until now.
Tegan: Why was the 2021 census a big deal?
Anne: Well, I would say each census is a big deal. The 2021 specifically, in terms of gender, is because it was the first time that we asked the gender question on the, on the census.
Tegan: This is, of course, our resident census expert.
Anne: I am Anne Milan. I am chief of the Census Demographic Variables Section in the center for Demography at Statistics Canada.
Tegan: Yeah. So you said it's the gender question. Could you elaborate? So what's different about the 2021 census that we didn't ask before?
Anne: There's two, two main changes. There is a precision of “at birth” added to the sex question, and the question on gender is completely new. So that asked someone to identify whether they were male, female and there was an “or please specify” category where persons could write in their response.
It was historic to, to include this information. It's the first time that, census data was released on the transgender and non-binary population among all the countries around the world. And so we're very proud of that.
Tegan: The census allowed people to write in whatever gender identity they would prefer. Why do that instead of boxes to tick?
Anne: With the gender question, we felt that having a write-in, or please specify this person's gender, was the most respectful and inclusive strategy to use so that people could select the gender that was most relevant to them.
Tegan: Do you have any idea how many different genders people put?
Anne: There were many.
Tegan: Many.
Anne: There were many,
Tegan: Many is a valid answer.
Tegan: Putting a blank space allowed respondents to describe themselves as they saw fit. And they did. StatCan uses the term non-binary as an umbrella term, but that's not necessarily how everyone describes themselves. Almost a third of those under this rainbow coloured non-binary umbrella used at different word to describe their gender: androgynous, bigender, intergender, pangender, polygender, queer, and two spirit. These were all terms provided by census respondents, but to be sure these weren't all of them. As Anne said, there were many many more.
Anne: If there was no gender question before, they wouldn't have had that opportunity to select their own gender. And that was one of the things that we noticed with the 2016 census when we were reviewing the comments that, uh, that people had put in, they were saying that the sex question, which had been there for many census cycles, was not precise enough for their needs. So some people felt that they were excluded. And of course, the goal of the census is to count all Canadians and have everybody see themselves in the data.
Tegan: Why is it important that people see themselves in the data?
Anne: Well, I think the, the census is a count of the total population in Canada. So of course it's important for people to see themselves in the data. We want people to have that experience and to feel that this information is relevant to them, that they're counted, that their voices matter. So really, that's the goal of the census: to include everybody and to have everybody feel included.
Tegan: So you said that people gave feedback in 2016, uh, saying that the census wasn't, asking necessarily the right question. Is that where the idea came from to make this change?
Anne: It was part of it. Each census cycle, we review all of the content, all of the questions, all of the definitions, and as part of that content determination process, there's an extensive consultation and engagement that takes place almost immediately after any census. And one of the common responses was that, gender was an information gap that, uh, that was needed.
And so following that, we had some more specific focus groups, individual, in-depth conversations. And this included all Canadians, cisgender, as well as transgender, non-binary persons. And so we, we took all that information into account. We developed the new content.
Tegan: What have we learned? What do the stats say about gender?
Anne: One in 300 Canadians aged 15 and over living in private households were transgender or non-binary individuals. Numerically, this is about a hundred thousand, so about 60,000 were transgender persons and about just over 40,000 were non-binary persons.
They tend to be younger on average than cisgender persons. So just to give you an idea about two thirds are less than age 35, so between age 15 and 34.
Tegan: There really is quite an age difference. Among those who were between 15 and 34, about one in 150 were transgender or non-binary. For those who were over the age of 35, it was only one in 550. That means, proportionally, there were more than three times as many transgender people aged 15 to 34 as there were 35 and over.
Anne: And it might be that, you know, younger people are more comfortable reporting their gender. From a generational perspective, attitudes and behaviors of a particular generation are informed by historical context in which they're raised. So age differences were a big trend that we noticed.
Tegan: How comfortable people are reporting their gender on an official government form was something Cyril cinder mentioned as well.
Cyril: I think a lot of people in the 2SLGBTQIA+ community are still somewhat apprehensive or nervous to, you know, really tell the government, yes, I am transgender, or, yes, I am non-binary due to historical systems of oppression and how they have impacted those communities.
Tegan: Age differences weren't the only notable finding in the data.
Anne: There were also regional differences. So, for example, among the largest urban areas, what we call census metropolitan areas, Victoria, Halifax, and Fredericton really stood out. And these three large urban areas had certain elements in common. They had stronger population growth between 2016 and 2021 compared to the national average. They had larger shares, 15 to 34 than the national average. And they're all home to several major universities and colleges. And of course students tend to have a, a younger age profile.
So those are some of the high level findings. And we were so pleased with the reaction to the information. It was overwhelmingly positive. And so we were very, very proud of that.
Cyril: I think it's so important. I mean, other levels of government, we are recognizing that transgender and non-binary people exist, right? We have the option for different gender markers, on our IDs, and, you know, normally when you go to a doctor's office, or you fill out any other demographic form, you're gonna have an opportunity to indicate what your gender identity is. And when we're talking about something as large-scale as the census, which drives so many of the decisions that we make at various levels of government. It informs, you know, grant money, who's getting what, how many resources need to be allocated to which communities. It's important to have an accurate measure of those communities. You know, Canada was the first country to include this in the census, but these calls have been going on for over a decade to be able to access this information.
But even just asking the question, it, it indicates that the government does care about transgender and non-binary Canadians, that our experiences are important and that we are part of Canadian society.
Tegan: From your perspective, now that we have these data about transgender Canadians, what should we do with it? How can it be put to best use? And what would your hopes be for next steps?
Cyril: Mm-hmm. I think some of the important things is, is being able to use this information about like where are resources needed in particular, right. I was looking through the data and things like the vast majority of non-binary people live in six urban centers in Canada.
Right? That is huge to know, but also to know like how many aging trans, non-binary people do we have in Canada? What kind of services might they need within the elder care system in this country, which is dealing with a lot of struggles, but how might these people's needs be unique? Where are they? Where are those services needed to go? What can effectively serve these communities? How can we support these people who we know are more likely to struggle with negative mental health impacts? And other research also shows us more likely to live in poverty, more likely to deal with other axes of systemic oppression and various things like that. Making the information publicly available is also very helpful because it allows us to use it for advocacy work. And I think also just kind of sometimes putting in context how much vitriol is directed towards the trans non-binary community and how few of us there actually are. Right? We are a small community. Looking at the data, there's just about a hundred thousand of us in the country and knowing how to support that. Putting in context of how large our population is actually becoming and at the same time , in terms of next steps I think it’s important to get more accurate data.
Tegan: This is why the census is so crucial. StatCan doesn't just gather data. Our experts also analyze them, and Cyril's not the only one looking forward to getting more information.
Anne: It's very exciting and as an analyst this is the part that I enjoy because all of the census variables are available now: education, labor, income. So we can do a deeper dive into some of the patterns. And that's exactly what we're doing now. So there's a paper that's currently underway on the socioeconomic wellbeing of transgender and non-binary population, looking at characteristics like education labor force participation, income, housing. So that's underway, and that will be available in the coming months. That's one activity that we're working on: in depth analysis.
And what's exciting about 2026 is we will have trends. 2021, it was, it was the excitement of having that data for the first time. But now we will have two time points so we can see what were the changes over time. And that will allow us to do even more interesting analysis.
Tegan: How often does the census change, and maybe more importantly, why does it change?
Anne: I would say that the census changes every census cycle. And that's what keeps it relevant. I mean, for, for over the past a hundred years, it continues to evolve as as society evolves, and that's what makes it exciting.
One example I can maybe give of content that has continued but also changed over time is the adult population 15 plus in couples. We've been measuring it for, for over a hundred years. So in 1921, a couple was a married couple. In 1981, we introduced the concept of common law couples. In 2001, we introduced the idea of same sex and opposite sex common law couples. Following national legislation that permitted same-sex couples to marry in 2005, we then counted same-sex married couples in 2006. So we have this increasing way to slice and dice the data, but we also have this continuity over time. And so then in 2021, we added this further element of being able to look at couples by gender. So whether a couple is comprised of one transgender person or one non-binary person. And so that ability to look at emerging family forms continues while maintaining that ability to look at historical trends as well.
Tegan: While on the subject of history and trends. It's important to make the point that even though 2021 was the first census to ask about gender, trans and non-binary Canadians have always been here.
Anne: I think there is a recognition that... that, for example, in this, in this situation, transgender and non-binary people have, have always existed, but it's our ability to measure it. And that's the, that's the new part.
Tegan: Just because you can't, you're not measuring something, doesn't mean it doesn't exist.
Anne: Exactly.
Tegan: Does the future look bright for trans and gender nonconforming Canadian kids? What opportunities and what challenges do you foresee?
Cyril: I, I think the future does look bright for Canadian transgender, non-binary, gender non-conforming youth. I, I think that there is something really, really wonderful ahead, but the path to that wonderful future currently has a lot of barriers in the way.
We have made incredible progress in recent decades as a community and we are seeing intense reactions to that progress from people who would like to see it clawed back and, you know, everything I ever let go had claw marks on it. So good luck with that initiative. I think a lot of us feel that way. But we cannot become complacent. We cannot, you know, pat ourselves on the back and say, job well done. The fight's over. We did it. And ignore what's actually happening on the ground. Because if we do that, we are going to lose that bright future.
We are going to repeat history and the repetition of history loses lives. People die in the circumstances that we have been living in for centuries at this point. And to me, that's not an acceptable way forward. It's, it's not okay to have our trans non-binary and gender non-conforming siblings lost in this fight.
Queer and trans kids should get to grow up to be queer and trans adults. And that should not be a matter of debate. That is asked and answered at this point. And we need to be firm in that and not fall into the paradox of tolerance, whereby tolerating intolerance, it is allowed to fester and grow and become cancerous and take over, and then all of a sudden, oh, where'd all those rights go that we fought so hard to win?
So, I do believe that the future is bright because I do believe that Canadians care about this, and I do believe that Canadians are intelligent and capable of understanding honest facts when they're placed in front of them, that we can dispel negative myths, that we can march forward together towards something that is better for all of us, but we need to put in the work to make sure that that happens.
Tegan: What is allyship to you and how can people be allies to the queer community?
Cyril: Allyship is active, not passive. A lot of people, you know, identify with the idea of allyship. They want to be an ally, and I think that's a wonderful thing. But when someone tells me like, oh, I'm an ally to the queer community, I'm like, great. What does that look like? What do you do to be an ally to the queer community? Because it's not enough to just not be homophobic, transphobic, and queer phobic. It's not enough to just not be a bigot. You have to oppose it in some way. You have to support the community in some way.
We can't be left alone just to fight for our own cause. We need our cisgender and heterosexual allies to also show up for us. And so allyship is an active thing. It is something you can be bestowed, something you can be granted from the community. You are an ally. You are showing up for us. You fight for us. You are willing to be uncomfortable if it means being able to protect our dignity and our personhood. That means a lot. It is not something that you can just claim.
Tegan: Is there anything you'd like to add?
Anne: Well, maybe just one more word about the value of the census in general. I see the value of it every day to us at StatCan, but also to broader Canadians. It's the best source of data for looking at smaller populations and subgroups, and of course, transgender and non-binary persons fall into that category. But there's many other smaller populations as well that's important to study. It's a valuable source for detailed and local geographies so that municipal planners can plan schools and hospitals and home care. As the concepts broaden, we often don't lose content, but it does allow us to, to integrate these new patterns that we're seeing. And so that makes it be able to evolve and maintain its relevance, and I think I can't finish without thanking all Canadians for their participation, for their input. It's very much appreciated and we certainly couldn't have the census without them.
Tegan: Thank you for your time. Thank you for your sharing your expertise.
Anne: Thank you.
Tegan: If someone would like to learn more about you and your work, maybe they'd be interested in seeing what drag is firsthand. Where can they go?
Cyril: Oh, so I have a website, www.cyrilcinder.com. C-Y-R-I-L-C-I-N-D-E-R. I'm also all over all the social medias, uh, Instagram, TikTok, Facebook, all of those required things. They can come support their local drag. That is, to me, the greatest, most important thing. It is your local drag artists, the ones who maybe don't get to be on tv, who are maybe a little bit more different. Who are the ones who are out working in your community, who I think have the most valuable things to say. Um, I saw my first drag show in 2014 and it opened my eyes so, so much, and I just hope more people can go have that experience.
Tegan: And if maybe someone's listening and questioning their own gender. Do you have any suggestions or resources to recommend?
Cyril: If you're sort of questioning your own gender identity, there has been so much work done writing done to help you with that experience. There are really wonderful books. Um, you and your Gender Identity by Hoffman-Fox is a great workbook that can, people look, can look through. It's often available at your local library. Your local library will have a lot of resources on gender identity and gender exploration for a variety of age groups. Um, you could look at Interligne, which is a 2SLGBTQIA+ listening service in Canada that are based out of Montreal. If you're indigenous, there are indigenous focused resources for exploring two-spirit identity, you know? Open yourself up, ask questions. Go to your local queer bookstore or your local queer venue if you have one. If you don't have one, the internet is a fantastic place to find some good free educational resources and support from other people who feel like you, because I promise you, no matter what questions you have, no matter what feeling you are struggling with, you are not alone in that experience, and there is somebody out there who's asking the exact same questions, and you don't deserve to go through that journey alone.
Tegan: Thank you so much for joining us. We really appreciate it.
Cyril: Thank you for having me
Tegan: You’ve been listening to Eh Sayers. Thank you to our guests, Cyril Cinder and Anne Milan. Molly’s Tuxedo was written by Vicki Johnson and illustrated by Gillian Reid. It was published by little bee books. Thank you for letting us share it on our show. It was read by Cyril
Cinder. If you’re interested in learning more about our census gender data, check out the links in the show notes.
You can subscribe to this show wherever you get your podcasts. There you can also find the French version of our show, called Hé-coutez bien. If you liked this show, please rate, review, and subscribe. Thanks for listening!
Sources:
The Daily - Canada is the first country to provide census data on transgender and non-binary people
Statement outlining results, risks and significant changes in operations, personnel and program
Statistics Canada ("the agency") is a member of the Innovation, Science and Industry portfolio.
Statistics Canada's role is to ensure that Canadians have access to a trusted source of statistics on Canada that meets their highest priority needs.
The agency's mandate derives primarily from the Statistics Act. The Act requires that the agency collects, compiles, analyzes and publishes statistical information on the economic, social, and general conditions of the country and its people. It also requires that Statistics Canada conduct the census of population and the census of agriculture every fifth year and protects the confidentiality of the information with which it is entrusted.
Statistics Canada also has a mandate to co-ordinate and lead the national statistical system. The agency is considered a leader, among statistical agencies around the world, in co–ordinating statistical activities to reduce duplication and reporting burden.
More information on Statistics Canada's mandate, roles, responsibilities and programs can be found in the 2023-2024 Main Estimates and in the Statistics Canada 2023-2024 Departmental Plan.
The Quarterly Financial Report:
Statistics Canada has the authority to collect and spend revenue from other federal government departments and agencies, as well as from external clients, for statistical services and products.
This quarterly report has been prepared by management using an expenditure basis of accounting. The accompanying Statement of Authorities includes the agency's spending authorities granted by Parliament and those used by the agency consistent with the Main Estimates for the 2023-2024 fiscal year. This quarterly report has been prepared using a special purpose financial reporting framework designed to meet financial information needs with respect to the use of spending authorities.
The authority of Parliament is required before moneys can be spent by the Government. Approvals are given in the form of annually approved limits through appropriation acts or through legislation in the form of statutory spending authority for specific purposes.
The agency uses the full accrual method of accounting to prepare and present its annual departmental financial statements that are part of the departmental results reporting process. However, the spending authorities voted by Parliament remain on an expenditure basis.
This section highlights the significant items that contributed to the net increase in resources available for the year, as well as actual expenditures for the quarter ended June 30.
This bar graph shows Statistics Canada's budgetary authorities and expenditures, in thousands of dollars, as of June 30, 2022 and 2023:
Chart 1 outlines the gross budgetary authorities, which represent the resources available for use for the year as of June 30.
Total authorities available for 2023-24 have increased by $43.1 million, or 6.2%, from the previous year, from $696.7 million to $739.8 million (Chart 1). The net increase is mostly the result of the following:
In addition to the appropriations allocated to the agency through the Main Estimates, Statistics Canada also has vote net authority within Vote 1, which entitles the agency to spend revenues collected from other federal government departments, agencies, and external clients to provide statistical services. The vote netting authority is stable at $120 million when comparing the first quarter of fiscal years 2022-2023 and 2023-2024.
Year-to-date net expenditures recorded to the end of the first quarter decreased by $0.4 million, or 0.2% from the previous year, from $185.3 million to $184.9 million (see Table A: Variation in Departmental Expenditures by Standard Object).
Statistics Canada spent approximately 29.8% of its authorities by the end of the first quarter, compared with 32.1% in the same quarter of 2022-2023.
| Departmental Expenditures Variation by Standard Object: | Q1 year-to-date variation between fiscal year 2022-2023 and 2023-2024 | |
|---|---|---|
| $'000 | % | |
| (01) Personnel | -6,633 | -3.9 |
| (02) Transportation and communications | 393 | 11.0 |
| (03) Information | 1 | 0.1 |
| (04) Professional and special services | 1,834 | 22.1 |
| (05) Rentals | -1,641 | -16.2 |
| (06) Repair and maintenance | -69 | -44.8 |
| (07) Utilities, materials and supplies | -140 | -65.1 |
| (08) Acquisition of land, buildings and works | - | N/A |
| (09) Acquisition of machinery and equipment | -1,141 | -72.5 |
| (10) Transfer payments | - | N/A |
| (12) Other subsidies and payments | -660 | -79.8 |
| Total gross budgetary expenditures | -8,056 | -4.1 |
| Less revenues netted against expenditures: | ||
| Revenues | -7,685 | -65.8 |
| Total net budgetary expenditures | -371 | -0.2 |
| Note: Explanations are provided for variances of more than $1 million. | ||
Personnel: The decrease is mainly due to spendings for seasonal, casual, and students' salaries, offset by a slight increase related to cost-recovery work following the dissemination of the 2021 Census of Population.
Professional and special services: The increase is mainly due to expenses with IT consultants and timing difference in invoicing compared to the first quarter of 2022-2023.
Rentals: The decrease is mainly due to a one-time invoice for a software licence paid in the first quarter of 2022-2023.
Acquisition of machinery and equipment: The decrease is mainly due to the purchase of computers in the first quarter of 2022-2023.
Revenues: The decrease is mainly due to a timing difference in invoicing compared to last year.
In 2023-2024, the following changes in operations and program activities are underway:
Statistics Canada will address the issues and corresponding uncertainties raised in this Quarterly Financial Report by implementing corresponding risk mitigation measures captured in the 2023-2024 Corporate Risk Profile and at the program level.
Statistics Canada continues to pursue and invest in modernizing business processes and tools to maintain its relevance and maximize the value it provides to Canadians. To address uncertainties, the agency is implementing the Census of Environment, the Quality of Life Framework for Canada and the Disaggregated Data Action Plan initiatives to meet the evolving needs of users and remain relevant as an agency. The agency is also remaining vigilant to cyber threats while supporting the use of modern methods with a functional digital infrastructure.
Statistics Canada requires a skilled workforce to achieve its objectives; however, it is difficult to compete with other organizations in the data ecosystem and the current labour market situation. To address uncertainties, Statistics Canada will create partnerships with other government departments, international organizations, and IT Industry partners to find innovative ways to collaborate on bridging gaps in digital skills and IT human resource shortfalls. The agency will continue promoting a strong workplace culture, a healthy work-life balance and advance on the Equity, Diversity and Inclusion Action Plan. In addition, it will focus on existing employees and continue its effort to achieve greater diversity and inclusion across its workforce and promote and support accessibility.
Statistics Canada continues its collaboration with federal partners to access IT services and support to realise its modernization objectives and to implement the Cloud Optimization Activities. To address uncertainties, the agency is working closely with its federal partners, while adhering to the agency's notable financial planning management practices, integrated strategic planning framework as well as strengthening its financial stewardship.
Approved by:
Anil Arora, Chief Statistician
Ottawa, Ontario
Signed on: August 23rd, 2023
Kathleen Mitchell, Chief Financial Officer
Ottawa, Ontario
Signed on: August 15th, 2023
| Fiscal year 2023-2024 | Fiscal year 2022–2023 | |||||
|---|---|---|---|---|---|---|
| Total available for use for the year ending March 31, 2024Table note * | Used during the quarter ended June 30, 2023 | Year-to-date used at quarter-end | Total available for use for the year ending March 31, 2023Table note * | Used during the quarter ended June 30, 2022 | Year-to-date used at quarter-end | |
| in thousands of dollars | ||||||
| Vote 1 — Net operating expenditures | 530,377 | 166,191 | 166,191 | 496,731 | 165,294 | 165,294 |
| Statutory authority — Contribution to employee benefit plans | 89,458 | 18,724 | 18,724 | 79,967 | 19,992 | 19,992 |
| Total budgetary authorities | 619,835 | 184,915 | 184,915 | 576,698 | 185,286 | 185,286 |
|
||||||
| Fiscal year 2023-2024 | Fiscal year 2022–2023 | |||||
|---|---|---|---|---|---|---|
| Planned expenditures for the year ending March 31, 2024 | Expended during the quarter ended June 30, 2023 | Year-to-date used at quarter-end | Planned expenditures for the year ending March 31, 2023 | Expended during the quarter ended June 30, 2022 | Year-to-date used at quarter-end | |
| in thousands of dollars | ||||||
| Expenditures: | ||||||
| (01) Personnel | 636,127 | 164,220 | 164,220 | 613,079 | 170,853 | 170,853 |
| (02) Transportation and communications | 11,992 | 3,979 | 3,979 | 11,745 | 3,586 | 3,586 |
| (03) Information | 8,682 | 1,340 | 1,340 | 9,041 | 1,339 | 1,339 |
| (04) Professional and special services | 48,413 | 10,120 | 10,120 | 35,898 | 8,286 | 8,286 |
| (05) Rentals | 21,089 | 8,487 | 8,487 | 17,160 | 10,128 | 10,128 |
| (06) Repair and maintenance | 972 | 85 | 85 | 475 | 154 | 154 |
| (07) Utilities, materials and supplies | 1,642 | 75 | 75 | 1,736 | 215 | 215 |
| (08) Acquisition of land, buildings and works | 557 | - | - | 555 | - | - |
| (09) Acquisition of machinery and equipment | 10,304 | 432 | 432 | 6,962 | 1,573 | 1,573 |
| (10) Transfer payments | - | - | - | - | - | - |
| (12) Other subsidies and payments | 57 | 167 | 167 | 47 | 827 | 827 |
| Total gross budgetary expenditures | 739,835 | 188,905 | 188,905 | 696,698 | 196,961 | 196,961 |
| Less revenues netted against expenditures: | ||||||
| Revenues | 120,000 | 3,990 | 3,990 | 120,000 | 11,675 | 11,675 |
| Total revenues netted against expenditures | 120,000 | 3,990 | 3,990 | 120,000 | 11,675 | 11,675 |
| Total net budgetary expenditures | 619,835 | 184,915 | 184,915 | 576,698 | 185,286 | 185,286 |
By David Chiumera, Statistics Canada
In recent years, the field of data science has experienced explosive growth, with businesses across many sectors investing heavily in data-driven solutions to optimize decision-making processes. However, the success of any data science project relies heavily on the quality of the code that underpins it. Writing production-level code is crucial to ensure that data science models and applications can be deployed and maintained effectively, enabling businesses to realize the full value of their investment in data science.
Production-level code refers to code that is designed to meet the needs of the end user, with a focus on scalability, robustness, and maintainability. This contrasts with code that is written purely for experimentation and exploratory purposes, which may not be optimized for production use. Writing production-level code is essential for data science projects as it allows for the efficient deployment of solutions into production environments, where they can be integrated with other systems and used to inform decision-making.
Production-level code has several key benefits for data science projects. Firstly, it ensures that data science solutions can be easily deployed and maintained. Secondly, it reduces the risk of errors, vulnerabilities, and downtime. Lastly, it facilitates collaboration between data scientists and software developers, enabling them to work together more effectively to deliver high-quality solutions. Finally, it promotes code reuse and transparency, allowing data scientists to share their work with others and build on existing code to improve future projects.
Overall, production-level code is an essential component of any successful data science project. By prioritizing the development of high-quality, scalable, and maintainable code, businesses can ensure that their investment in data science delivers maximum value, enabling them to make more informed decisions and gain a competitive edge in today's data-driven economy.
The scope of data science is vast, encompassing a broad range of techniques and tools used to extract insights from data. At its core, data science involves the collection, cleaning, and analysis of data to identify patterns and make predictions. Its applications are numerous, ranging from business intelligence and marketing analytics to healthcare and scientific research. Data science is used to solve a wide range of problems, such as predicting consumer behavior, detecting fraud, optimizing operations, and improving healthcare outcomes. As the amount of data generated continues to grow, the scope of data science is expected to expand further, with increasing emphasis on the use of advanced techniques such as machine learning and artificial intelligence.
Proper programming and software engineering practices are essential for building robust data science applications that can be deployed and maintained effectively. Robust applications are those that are reliable, scalable, and efficient, with a focus on meeting the needs of the end user. There are several types of programming and software engineering practices that are particularly important in the context of data science, such as version control, automated testing, documentation, security, code optimization, and proper use of design patterns to name a few.
By following proper practices, data scientists can build robust applications that are reliable, scalable, and efficient, with a focus on meeting the needs of the end user. This is critical for ensuring that data science solutions deliver maximum value to businesses and other organizations.
The ADP project is a Field 7 application that required involvement from the Data Science Division to refactor a citizen developed component due to a variety of issues that were negatively impacting its production readiness. Specifically, the codebase used to integrate workflows external to the system was found to be lacking in adherence to established programming practices, leading to a cumbersome and difficult user experience. Moreover, there was a notable absence of meaningful feedback from the program upon failure, making it difficult to diagnose and address issues.
Further exacerbating the problem, the codebase was also found to be lacking in documentation, error logging, and meaningful error messages for users. The codebase was overly coupled, making it difficult to modify or extend the functionality of the program as needed, and there were no unit tests in place to ensure reliability or accuracy. Additionally, the code was overfitted to a single example, which made it challenging to generalize to other use cases, there were also several desired features that were not present to meet the needs of the client.
Given these issues, the ability for the ADP project to pre-process semi-structured data was seriously compromised. The lack of feedback and documentation made it exceedingly difficult for the client to use the integrated workflows effectively, if at all, leading to frustration and inefficiencies. The program outputs were often inconsistent with expectations, and the absence of unit tests meant that reliability and accuracy were not assured. In summary, the ADP project's need for a refactor of the integrated workflows (a.k.a. clean-up or redesign) was multifaceted and involved addressing a range of programming and engineering challenges to ensure a more robust and production-ready application. To accomplish this, we used a Red Green refactoring approach to improve the quality of the product.
Refactoring is the process of restructuring existing code in order to improve its quality, readability, maintainability, and performance. This can involve a variety of activities, including cleaning up code formatting, eliminating code duplication, improving naming conventions, and introducing new abstractions and design patterns.
There are several reasons why refactoring is beneficial. Firstly, it can improve the overall quality of the codebase, making it easier to understand and maintain. This can save time and effort over the long term, especially as codebases become larger and more complex. Additionally, refactoring can improve performance and reduce the risk of bugs or errors, leading to a more reliable and robust application.
One popular approach to refactoring is the "Red Green" approach, as part of the test-driven development process. In the Red Green approach, a failing test case is written before any code is written or refactored. This failing test is then followed by writing the minimum amount of code required to make the test pass, before proceeding to refactor the code to a better state if necessary. In contrast, the Green Red approach is the reverse of this, where the code is written before the test cases are written and run.
The benefits of the Red Green approach include the ability to catch errors early in the development process, leading to fewer bugs and more efficient development cycles. The approach also emphasizes test-driven development, which can lead to more reliable and accurate code. Additionally, it encourages developers to consider the user experience from the outset, ensuring that the codebase is designed with the end user in mind.
The first step, the Red component, refers to writing a test that fails. From here the code is modified to make the test pass, which refers to the Green component. Lastly, any refactoring that needs to be done to further improve the codebase is done, another test is created and run to test which fails, this is the red component again. The cycle continues indefinitely until the desired state is reached terminating the feedback loop.
In the case of the ADP project, the Red Green approach was applied during the refactoring process. This led to a smooth deployment process, with the application being more reliable, robust, and easier to use. By applying this approach, we were able to address the various programming and engineering challenges facing the project, resulting in a more efficient, effective, stable, and production-ready application.
While data science has become a critical field in many industries, it is not without its challenges. One of the biggest issues is the lack of standard practices that are often missing in data science work. While there are many standard practices that can improve the quality, maintainability, and reproducibility of data science code, many data scientists overlook them in favor of quick solutions.
This section will cover some of the most important standard practices that are often missing in data science work. These include:
By following these standard practices, data scientists can improve the quality and reliability of their code, reduce errors and bugs, and make their work more accessible to others.
Documenting code is crucial for making code understandable and usable by other developers. In data science, this can include documenting data cleaning, feature engineering, model training, and evaluation steps. Without proper documentation, it can be difficult for others to understand what the code does, what assumptions were made, and what trade-offs were considered. It can also make it difficult to reproduce results, which is a fundamental aspect of scientific research as well as building robust and reliable applications.
Docstrings are strings that provide documentation for functions, classes, and modules. They are typically written in a special format that can be easily parsed by tools like Sphinx to generate documentation. Writing clear docstrings can help other developers understand what a function or module does, what arguments it takes, and what it returns. It can also provide examples of how to use the code, which can make it easier for other developers to integrate the code into their own projects.
def complex (real=0.0, imag=0.0):
"""Form a complex number.
Keyword arguments:
real -- the real part (default 0.0)
imag -- the imaginary part (default 0.0)
"""
if imag == 0.0 and real == 0.0:
return compelx_zero
...
Multi-Line Docstring Example
Style guidelines in code play a crucial role in ensuring readability, maintainability, and consistency across a project. By adhering to these guidelines, developers can enhance collaboration and reduce the risk of errors. Consistent indentation, clear variable naming, concise commenting, and following established conventions are some key elements of effective style guidelines that contribute to producing high-quality, well-organized code. An example of this are PEP (Python Enhancement Proposal) standards, which provide guidelines and best practices for writing Python code. It ensures that code can be understood by other Python developers, which is important in collaborative projects but also for general maintainability. Some PEP standards address naming conventions, code formatting, and how to handle errors and exceptions.
Type hints are annotations that indicate the type of a variable or function argument. They are not strictly necessary for Python code to run, but they can improve code readability, maintainability, and reliability. Type hints can help detect errors earlier in the development process and make code easier to understand by other developers. They also provide better interactive development environment (IDE) support and can improve performance by allowing for more efficient memory allocation.
Version control is the process of managing changes to code and other files over time. It allows developers to track and revert changes, collaborate on code, and ensure that everyone is working with the same version of the code. In data science, version control is particularly important because experiments can generate large amounts of data and code. By using version control, data scientists can ensure that they can reproduce and compare results across different versions of their code and data. It also provides a way to track and document changes, which can be important for compliance and auditing purposes.
A master branch (V1) is created as the main project. A new branch off shooting V1 is created in order to develop and test until the modifications are ready to be merged with V1, creating V2 of the master branch. V2 is then released.
Testing code is the formal (and sometimes automated) verification of the completeness, quality, and accuracy of code against expected results. Testing code is essential for ensuring that the codebase works as expected and can be relied upon. In data science, testing can include unit tests for functions and classes, integration tests for models and pipelines, and validation tests for datasets. By testing code, data scientists can catch errors and bugs earlier in the development process and ensure that changes to the code do not introduce new problems. This can save time and resources in the long run by reducing the likelihood of unexpected errors and improving the overall quality of the code.
Code reviews are a process in which other developers review new code and code changes to ensure that they meet quality and style standards, are maintainable, and meet the project requirements. In data science, code reviews can be particularly important because experiments can generate complex code and data, and because data scientists often work independently or in small teams. Code reviews can catch errors, ensure that code adheres to best practices and project requirements, and promote knowledge sharing and collaboration among team members.
Reproducibility is a critical aspect of scientific research and data science. Reproducible results are necessary for verifying and building on previous research, and for ensuring that results are consistent, valid and reliable. In data science, ensuring reproducibility can include documenting code and data, using version control, rigorous testing, and providing detailed instructions for running experiments. By ensuring reproducibility, data scientists can make their results more trustworthy and credible and can increase confidence in their findings.
Logging refers to the act of keeping a register of events that occur in a computer system. This is important for troubleshooting, information gathering, security, providing audit info, among other reasons. It generally refers to writing messages to a log file. Logging is a crucial part of developing robust and reliable software, including data science applications. Logging errors can help identify issues with the application, which in turn helps to debug and improve it. By logging errors, developers can gain visibility into what went wrong in the application, which can help them diagnose the problem and take corrective action.
Logging also enables developers to track the performance of the application over time, allowing them to identify potential bottlenecks and areas for improvement. This can be particularly important for data science applications that may be dealing with large datasets or complex algorithms.
Overall, logging is an essential practice for developing and maintaining high-quality data science applications.
When it comes to data science applications, performance is often a key consideration. To ensure that the application is fast and responsive, it's important to write code that is optimized for speed and efficiency.
One way to achieve this is by writing low-overhead code. Low-overhead code is code that uses minimal resources and has a low computational cost. This can help to improve the performance of the application, particularly when dealing with large datasets or complex algorithms.
Writing low-overhead code requires careful consideration of the algorithms and data structures used in the application, as well as attention to detail when it comes to memory usage and processing efficiency. Thought should be given to the system needs and overall architecture and design of a system up front to avoid major design changes down the road.
Additionally, low-overhead code is easily maintained requiring infrequent reviews and updates. This is important as it reduces the cost to maintain systems and allows for more focused development on improvements or new solutions.
Overall, writing low-overhead code is an important practice for data scientists looking to develop fast and responsive applications that can handle large datasets and complex analyses while keeping maintenance costs low.
Data validation is the process of checking that the input data meets certain requirements or standards. Data validation is another important practice in data science as it can help to identify errors or inconsistencies in the data before they impact the analysis or modeling process.
Data validation can take many forms, from checking that the data is in the correct format to verifying that it falls within expected ranges or values. Different types of data validation checks exist, such as type, format, correctness, consistency, and uniqueness. By validating data, data scientists can ensure that their analyses are based on accurate and reliable data, which can improve the accuracy and credibility of their results.
Continuous Integration and Continuous Deployment (CI/CD) is a set of best practices for automating the process of building, testing, and deploying software. CI/CD can help to improve the quality and reliability of data science applications by ensuring that changes are tested thoroughly and deployed quickly and reliably.
CI/CD involves automating the process of building, testing, and deploying software, often using tools and platforms such as Jenkins, GitLab, or GitHub Actions. By automating these processes, developers can ensure that the application is built and tested consistently, and that any errors or issues that block deployment of problematic code are identified and addressed quickly.
CI/CD can also help to improve collaboration among team members, by ensuring that changes are integrated and tested as soon as they are made, rather than waiting for a periodic release cycle.
The image illustrates a repeating process represented by the infinity symbol sectioned into 8 unequal parts. Starting from the middle and moving counterclockwise the first of these parts are: plan, code, build, and continuous testing. Then continuing from the last piece, which was in the center, moving clockwise the parts are: release, deploy, operate, and then monitor, before moving back to the original state of plan.
Overall, CI/CD is an important practice for data scientists looking to develop and deploy high-quality data science applications quickly and reliably.
In summary, production-level code is critical for data science projects and applications. Proper programming practices and software engineering principles such as adhering to PEP standards, using type hints, writing clear docstrings, version control, testing code, logging errors, validating data, writing low-overhead code, implementing continuous integration and continuous deployment (CI/CD), and ensuring reproducibility are essential for creating robust, maintainable, and scalable applications.
Not following these practices can result in difficulties such as a lack of documentation, no error logging, no meaningful error messages for users, highly coupled code, overfitted code to a single example, lacking features desired by clients, and failure to provide feedback upon failure. These issues can severely impact production readiness and frustrate users. If a user is frustrated, then productivity will be impacted and result in negative downstream impacts on businesses’ ability to effectively deliver their mandate.
The most practical tip for implementing production-level code is to work together, assign clear responsibilities and deadlines, and understand the importance of each of these concepts. By doing so, it becomes easy to implement these practices in projects and create maintainable and scalable applications.
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Hardware-based protection of data in use that can be applied anywhere
By: Betty Ann Bryanton, Canada Revenue Agency
The increasing popularity of connected devices and the prevalence of technologies, such as cloud, mobile computing, and the Internet of Things (IoT), has strained existing security capabilities and exposed "gaps in data security" (Lowans, 2020). Organizations that handle Personally Identifiable Information (PII) must "mitigate threats that target the confidentiality and integrity of either the application, or the data in system memory" (The Confidential Computing Consortium, 2021).
As a result, Gartner predicts, "by 2025, 50% of large organizations will adopt privacy-enhancing computation (PEC)Footnote1 for processing data in untrusted environmentsFootnote2 and multiparty data analytics use cases" (Gartner, 2020). Of the several PEC techniques, Trusted Execution Environment is the only one that relies on hardware to accomplish its privacy-enhancing goal.
A Trusted Execution Environment (TEE), or Secure Enclave as they are sometimes known, is an environment built with special hardware modules that allows for a secure area inside the device. This isolated environment runs in parallel with the operating system (OS). Input is passed into the TEE and computation is performed within the TEE ('secure world'), thereby protected from the rest of the untrusted system ('normal world'). These secure and isolated environments protect content confidentiality and integrity, preventing unauthorizedFootnote3 access to, or modification of, applications and data while in use.
The term 'confidential computing' is often used synonymously with TEE; they are related, but distinct. As per the Confidential Computing Consortium (CCC),Footnote4 confidential computing is enabled by the TEE; further, confidential computing provided by the hardware-based TEE is independent of topographical location (no mention of cloud, a user's device, etc.), processors (a regular processor or a separate one), or isolation techniques (e.g., whether encryption is used).
"Security is only as strong as the layers below it, since security in any layer of the compute stack could potentially be circumvented by a breach at an underlying layer" (The Confidential Computing Consortium, 2021). By moving security to its lowest silicon level, this reduces potential compromise since it minimizes dependencies higher in the stack (e.g., from the OS, peripherals, and their administrators and vendors).
Using a TEE allows a massive range of functionality to be provided to the user, while still meeting the requirements of privacy and confidentiality, without risking data when it is decrypted during processing. This allows users to secure intellectual property and ensure that PII is inaccessible. This protects against insider threats, attackers running malicious code, or unknown cloud providers. As such, TEEs represent a crucial layer in a layered security approach (aka defense-in-depth) and "have the potential to significantly boost the security of systems" (Lindell, 2020).
A TEE "can be applied anywhere including public cloud servers, on-premises servers, gateways, IoT devices, EdgeFootnote5 deployments, user devices, etc." (The Confidential Computing Consortium, 2021).
As per Confidential Computing: Hardware-Based Trusted Execution for Applications and Data, below is a summary of possible use cases for a TEE.
TEEs are provided by solutions such as Intel's Software Guard eXtensions (SGX) or ARM's TrustZone; via hardware vendor Software Development Kits (SDKs); or with abstraction layers (e.g., Google's Asylo) that eliminate the requirement to code explicitly for a TEE.
Many cloud vendors (e.g., Alibaba, Microsoft, IBM, and Oracle) are now providing TEE capabilities as a dedicated low-level service aligned with their computation offerings. However, due to lack of standardization, the specifications offered by cloud vendors should be closely examined to ensure they meet the organization's desired privacy and security requirements (Fritsch, Bartley, & Ni, 2020).
While protecting sensitive data poses significant architecture, governance, and technology challenges, using a TEE may provide a starting point for an alternative means of enhancing security from the lowest level.
However, a TEE is not plug-and-play; it is a technically challenging mechanism that "should be reserved for the highest-risk use cases" (Lowans, 2020). Nonetheless, "it is certainly harder to steal secrets from inside [a secured TEE than from the unsecured 'normal world']. It makes the attacker's job harder, and that is always a good thing" (Lindell, 2020).
Homomorphic Encryption, Secure Multiparty Computation, differential privacy, data anonymization, Trusted Platform Module.
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This survey collects data on capital expenditures in Canada. The information is used by federal and provincial government departments and agencies, trade associations, universities and international organizations for policy development and as a measure of regional economic activity.
Your information may also be used by Statistics Canada for other statistical and research purposes.
Your participation in this survey is required under the authority of the Statistics Act.
Data are collected under the authority of the Statistics Act, Revised Statutes of Canada, 1985, Chapter S-19.
By law, Statistics Canada is prohibited from releasing any information it collects that could identify any person, business, or organization, unless consent has been given by the respondent, or as permitted by the Statistics Act. Statistics Canada will use the information from this survey for statistical purposes only.
To enhance the data from this survey and to reduce the reporting burden, Statistics Canada may combine the acquired data with information from other surveys or from administrative sources.
To reduce respondent burden, Statistics Canada has entered into data-sharing agreements with provincial and territorial statistical agencies and other government organizations, which have agreed to keep the data confidential and use them only for statistical purposes. Statistics Canada will only share data from this survey with those organizations that have demonstrated a requirement to use the data.
Section 11 of the Statistics Act provides for the sharing of information with provincial and territorial statistical agencies that meet certain conditions. These agencies must have the legislative authority to collect the same information, on a mandatory basis, and the legislation must provide substantially the same provisions for confidentiality and penalties for disclosure of confidential information as the Statistics Act. Because these agencies have the legal authority to compel businesses to provide the same information, consent is not requested and businesses may not object to the sharing of the data.
For this survey, there are Section 11 agreements with the provincial and territorial statistical agencies of Newfoundland and Labrador, Nova Scotia, New Brunswick, Quebec, Ontario, Manitoba, Saskatchewan, Alberta, British Columbia, and the Yukon. The shared data will be limited to information pertaining to business establishments located within the jurisdiction of the respective province or territory.
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Chief Statistician of Canada
Statistics Canada
Attention of Director, Enterprise Statistics Division
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For this survey, there are Section 12 agreements with the statistical agencies of Prince Edward Island, the Northwest Territories and Nunavut as well as Environment and Climate Change Canada, Infrastructure Canada, the Canada Energy Regulator, Natural Resources Canada and Sustainability Development Technology Canada.
For agreements with provincial and territorial government organizations, the shared data will be limited to information pertaining to business establishments located within the jurisdiction of the respective province or territory.
1. Verify or provide the business or organization's legal and operating name and correct where needed.
Note: Legal name modifications should only be done to correct a spelling error or typo.
Legal Name
The legal name is one recognized by law, thus it is the name liable for pursuit or for debts incurred by the business or organization. In the case of a corporation, it is the legal name as fixed by its charter or the statute by which the corporation was created.
Modifications to the legal name should only be done to correct a spelling error or typo.
To indicate a legal name of another legal entity you should instead indicate it in question 3 by selecting 'Not currently operational' and then choosing the applicable reason and providing the legal name of this other entity along with any other requested information.
Operating Name
The operating name is a name the business or organization is commonly known as if different from its legal name. The operating name is synonymous with trade name.
Legal name
Operating name (if applicable)
2. Verify or provide the contact information of the designated business or organization contact person for this questionnaire and correct where needed.
Note: The designated contact person is the person who should receive this questionnaire. The designated contact person may not always be the one who actually completes the questionnaire.
First name
Last name
Title
Preferred language of communication
Mailing address (number and street)
City
Province, territory or state
Postal code or ZIP (Zone Improvement Plan) code
Example: A9A 9A9 or 12345-1234
Email address
Example: user@example.gov.ca
Telephone number (including area code)
Example: 123-123-1234
Extension number (if applicable)
Fax number (including area code)
Example: 123-123-1234
3. Verify or provide the current operational status of the business or organization identified by the legal and operating name above.
Why is this business or organization not currently operational?
When did this business or organization close for the season?
When does this business or organization expect to resume operations?
When did this business or organization cease operations?
Why did this business or organization cease operations?
Specify the other reasons why the operations ceased.
When was this business or organization sold?
What is the legal name of the buyer?
When did this business or organization amalgamate?
What is the legal name of the resulting or continuing business or organization?
What are the legal names of the other amalgamated businesses or organizations?
When did this business or organization become temporarily inactive?
When does this business or organization expect to resume operations?
Why is this business or organization temporarily inactive?
When did this business or organization cease operations?
Why did this business or organization cease operations?
4. Verify or provide the current main activity of the business or organization identified by the legal and operating name above.
Note: The described activity was assigned using the North American Industry Classification System (NAICS).
This question verifies the business or organization's current main activity as classified by the North American Industry Classification System (NAICS). The North American Industry Classification System (NAICS) is an industry classification system developed by the statistical agencies of Canada, Mexico and the United States. Created against the background of the North American Free Trade Agreement, it is designed to provide common definitions of the industrial structure of the three countries and a common statistical framework to facilitate the analysis of the three economies. NAICS is based on supply-side or production-oriented principles, to ensure that industrial data, classified to NAICS , are suitable for the analysis of production-related issues such as industrial performance.
The target entity for which NAICS is designed are businesses and other organizations engaged in the production of goods and services. They include farms, incorporated and unincorporated businesses and government business enterprises. They also include government institutions and agencies engaged in the production of marketed and non-marketed services, as well as organizations such as professional associations and unions and charitable or non-profit organizations and the employees of households.
The associated NAICS should reflect those activities conducted by the business or organizational units targeted by this questionnaire only, as identified in the 'Answering this questionnaire' section and which can be identified by the specified legal and operating name. The main activity is the activity which most defines the targeted business or organization's main purpose or reason for existence. For a business or organization that is for-profit, it is normally the activity that generates the majority of the revenue for the entity.
The NAICS classification contains a limited number of activity classifications; the associated classification might be applicable for this business or organization even if it is not exactly how you would describe this business or organization's main activity.
Please note that any modifications to the main activity through your response to this question might not necessarily be reflected prior to the transmitting of subsequent questionnaires and as a result they may not contain this updated information.
The following is the detailed description including any applicable examples or exclusions for the classification currently associated with this business or organization.
Description and examples
Provide a brief but precise description of this business or organization's main activity.
e.g. , breakfast cereal manufacturing, shoe store, software development
5. You indicated that is not the current main activity.
Was this business or organization's main activity ever classified as:?
6. Search and select the industry classification code that best corresponds to this business or organization's main activity.
How to search:
Select this business or organization's activity sector (optional)
Enter keywords or a brief description, then press the Search button
1. What are the start and end dates of this organization's 2023 fiscal year?
Note: For this survey, the end date should fall between April 1, 2023 and March 31, 2024.
Here are twelve common fiscal periods that fall within the targeted dates:
Here are other examples of fiscal periods that fall within the required dates:
Fiscal Year Start
Example: YYYY-MM-DD
Fiscal Year-End
Example: YYYY-MM-DD
2. What is the reason the reporting period does not cover a full year?
Select all that apply.
Seasonal operations
New business
Change of ownership
Temporarily inactive
Change of fiscal year
Ceased operations
Other reason - specify:
3. Throughout this questionnaire, please report financial information in thousands of Canadian dollars.
For example, an amount of $763,880.25 should be reported as:
CAN$ '000
I will report in the format above.
4. For the fiscal year 2023, what are this organization's preliminary estimates for capital expenditures?
Include: all capitalized overhead and capitalized interest.
If there are no capital expenditures, please enter '0'.
A. Oil and gas rights acquisition and retention costs (exclude inter-company sales or transfers):
Include acquisition costs and fees for oil and gas rights (include bonuses, legal fees and filing fees), and oil and gas retention costs.
B. Exploration and evaluation, capitalized or expensed ( e.g. , leases and licences, seismic, exploration drilling):
These expenditures include mineral rights fees and retention costs, geological, geophysical and seismic expenses, exploration drilling, and other costs incurred during the reporting period in order to determine whether oil or gas reserves exist and can be exploited commercially. Report gross expenditures, before deducting any incentive grants, incurred for oil and gas activities on a contracted basis and/or by your own employees. Exclude the cost of land acquired from other oil and gas companies.
C. Building construction ( e.g. , process building, office building, camp, storage building, and maintenance garage):
Include capital expenditures on buildings such as office buildings, camps, warehouses, maintenance garages, workshops, and laboratories. Fixtures, facilities and equipment that are integral parts of the building are included.
D. Other construction assets ( e.g. , development drilling and completions, processing facilities, natural gas plants, upgraders):
Include all infrastructure, other than buildings, such as the cost of well pads, extraction and processing infrastructure and plants, upgrading units, transportation infrastructure, water and sewage infrastructure, tailings, pipelines and wellhead production facilities (pumpjacks, separators, etc. ). Include all preconstruction planning and design costs such as development drilling, regulatory approvals, environmental assessments, engineering and consulting fees and any materials supplied to construction contractors for installation, as well as site clearance and preparation. Equipment which is installed as an integral or built-in feature of a fixed structure ( e.g. , casings, tanks, steam generators, pumps, electrical apparatus, separators, flow lines, etc. ) should be reported with the construction asset; however, when the equipment is replaced within an existing structure, the replacement cost should be reported in machinery and equipment (sustaining capital).
E. Machinery and equipment purchases ( e.g. , trucks, shovels, computers, etc. ):
Include transportation equipment for people and materials, computers, software, communication equipment, and processing equipment not included in the above categories.
| 2023 Preliminary Estimate ( CAN$ '000 ) | |
|---|---|
| Oil and gas rights acquisitions and retention costs | |
| Exploration and evaluation | |
| Non-residential building construction | |
| Development and other construction | |
| Machinery and equipment | |
| Total |
Total
5. For the fiscal year 2023, did this organization perform scientific research and development in Canada of at least $10,000 or outsource (contract-out) to another organization scientific research and development activities of at least $10,000?
Research and experimental development (R&D) comprise creative and systematic work undertaken in order to increase the stock of knowledge - including knowledge of humankind, culture and society - and to devise new applications of available knowledge. For an activity to be an R&D activity, it must satisfy five core criteria:
The term R&D covers three types of activity: basic research, applied research and experimental development. Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundations of phenomena and observable facts, without any particular application or use in view. Applied research is original investigation undertaken in order to acquire new knowledge. It is, however, directed primarily towards a specific, practical aim or objective. Experimental development is systematic work, drawing on knowledge gained from research and practical experience and producing additional knowledge, which is directed to producing new products or processes or to improving existing products or processes.
6. For the fiscal year 2024, what are this organization's intentions for capital expenditures?
Include: all capitalized overhead and capitalized interest.
If there are no capital expenditures, please enter '0'.
A. Oil and gas rights acquisition and retention costs (exclude inter-company sales or transfers):
Include acquisition costs and fees for oil and gas rights (include bonuses, legal fees and filing fees), and oil and gas retention costs
B. Exploration and evaluation, capitalized or expensed ( e.g. , leases and licences, seismic, exploration drilling):
These expenditures include mineral rights fees and retention costs, geological, geophysical and seismic expenses, exploration drilling, and other costs incurred during the reporting period in order to determine whether oil or gas reserves exist and can be exploited commercially. Report gross expenditures, before deducting any incentive grants, incurred for oil and gas activities on a contracted basis and/or by your own employees. Exclude the cost of land acquired from other oil and gas companies.
C. Building construction ( e.g. , process building, office building, camp, storage building, and maintenance garage):
Include capital expenditures on buildings such as office buildings, camps, warehouses, maintenance garages, workshops, and laboratories. Fixtures, facilities and equipment that are integral parts of the building are included.
D. Other construction assets ( e.g. , development drilling and completions, processing facilities, natural gas plants, upgraders):
Include all infrastructure, other than buildings, such as the cost of well pads, extraction and processing infrastructure and plants, upgrading units, transportation infrastructure, water and sewage infrastructure, tailings, pipelines and wellhead production facilities (pumpjacks, separators, etc. ). Include all preconstruction planning and design costs such as development drilling, regulatory approvals, environmental assessments, engineering and consulting fees and any materials supplied to construction contractors for installation, as well as site clearance and preparation. Equipment which is installed as an integral or built-in feature of a fixed structure ( e.g. , casings, tanks, steam generators, pumps, electrical apparatus, separators, flow lines, etc. ) should be reported with the construction asset; however, when the equipment is replaced within an existing structure, the replacement cost should be reported in machinery and equipment (sustaining capital).
E. Machinery and equipment purchases ( e.g. , trucks, shovels, computers, etc. ):
Include transportation equipment for people and materials, computers, software, communication equipment, and processing equipment not included in the above categories.
| Intentions 2023 ( CAN$ '000 ) | |
|---|---|
| Oil and gas rights acquisitions and retention costs | |
| Exploration and evaluation | |
| Non-residential building construction | |
| Development and other construction | |
| Machinery and equipment | |
| Total |
Total
In order to reduce future follow-up, please select one of the following options.
You could also make corrections to the current cycle by pressing the Previous button.
You have not reported anything for 2024, but have entered data for 2023. Is this correct? If you do not intend on having any capital expenditures in the 2024 fiscal year, please return to the previous page and enter `0`s. If this information is not yet available, please press the Next button.
7. You have not reported any capital expenditure intentions for 2024.
Please indicate the reason.
8. For the 2024 fiscal year, does this organization plan on performing scientific research and development in Canada of at least $10,000 or outsourcing (contracting-out) to another organization scientific research and development activities of at least $10,000?
Research and experimental development (R&D) comprise creative and systematic work undertaken in order to increase the stock of knowledge - including knowledge of humankind, culture and society - and to devise new applications of available knowledge. For an activity to be an R&D activity, it must satisfy five core criteria:
The term R&D covers three types of activity: basic research, applied research and experimental development. Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundations of phenomena and observable facts, without any particular application or use in view. Applied research is original investigation undertaken in order to acquire new knowledge. It is, however, directed primarily towards a specific, practical aim or objective. Experimental development is systematic work, drawing on knowledge gained from research and practical experience and producing additional knowledge, which is directed to producing new products or processes or to improving existing products or processes.
9. Does this business have a website?
Specify the business website address
e.g. , www.example.ca
Specify the business website address
e.g. , www.example.ca
Specify the business website address
e.g. , www.example.ca
Statistics Canada engages in web-data extraction, also known as web scraping, which is a process by which information is gathered and copied from the Web using automated scripts or robots, for retrieval and analysis. As a result, we may visit the website for this organization to search for and compile additional information. The use of web scraping is part of a broader effort to reduce the response burden on organizations, as well as produce additional statistical indicators to ensure that our data remain accurate and relevant.
We will strive to ensure that the data collection does not interfere with the functionality of the website. Any data collected will be used by Statistics Canada for statistical and research purposes only, in accordance with the agency’s privacy and confidentiality mandate.
More information regarding Statistics Canada’s web scraping initiative- this link will open in a new window.
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If you have any questions or concerns, please contact Statistics Canada Client Services, toll-free at 1-877-949-9492 [Teletypewriter or Telecommunication device for the deaf/teletype machine (TTY): 1-800-363-7629] or by email at infostats@statcan.gc.ca - this link will open in a new window. Additional information about this survey can be found by selecting the following link: Annual Capital Expenditures Survey: Preliminary Estimate for 2023 and Intentions for 2024.
10. Indicate any changes or events that affected the reported values for this business or organization, compared with the last reporting period.
Select all that apply.
11. Statistics Canada may need to contact the person who completed this questionnaire for further information.
Is [Provided Given Names], [Provided Family Name] the best person to contact?
Who is the best person to contact about this questionnaire?
First name:
Last name:
Title:
Email address:
Example: user@example.gov.ca
Telephone number (including area code):
Example: 123-123-1234
Extension number (if applicable):
The maximum number of characters is 5.
Fax number (including area code):
Example: 123-123-1234
12. How long did it take to complete this questionnaire?
Include the time spent gathering the necessary information.
13. Do you have any comments about this questionnaire?
Enter your comments
200 characters available
| Month | Sales of goods manufactured | Raw materials and components inventories | Goods / work in process inventories | Finished goods manufactured inventories | Unfilled Orders |
|---|---|---|---|---|---|
| % | |||||
| June 2022 | 0.68 | 1.16 | 1.52 | 1.76 | 1.44 |
| July 2022 | 0.69 | 1.11 | 1.76 | 1.52 | 1.36 |
| August 2022 | 0.68 | 1.14 | 1.76 | 1.58 | 1.36 |
| September 2022 | 0.66 | 1.07 | 1.83 | 1.58 | 1.48 |
| October 2022 | 0.66 | 1.10 | 1.82 | 1.55 | 1.48 |
| November 2022 | 0.65 | 1.10 | 1.68 | 1.58 | 1.46 |
| December 2022 | 0.61 | 1.08 | 1.89 | 1.57 | 1.47 |
| January 2023 | 0.65 | 1.14 | 1.81 | 1.40 | 1.47 |
| February 2023 | 0.68 | 1.15 | 1.87 | 1.39 | 1.53 |
| March 2023 | 0.66 | 1.11 | 1.73 | 1.39 | 1.44 |
| April 2023 | 0.69 | 1.09 | 1.61 | 1.35 | 1.42 |
| May 2023 | 0.68 | 1.11 | 1.71 | 1.44 | 1.43 |
| June 2023 | 0.70 | 1.11 | 1.77 | 1.49 | 1.45 |
For this consultation, members of the Canadian public and international partners were invited to review and provide feedback on Statistics Canada's gender and sexual diversity statistical metadata standardsFootnote 1. Specifically, Statistics Canada was seeking feedback on proposed updates to the standard for gender of person and new standards for sexual orientation and LGBTQ2+Footnote 2 status. Statistical standards for gender and sexual diversity (such as the definition of each concept and the classification which establishes its categories) allow for the reporting of statistically diverse groups of the population in a consistent manner. This report summarizes the feedback received from the consultation. For more information on statistical standards as well as the additional engagement activities that took place to inform standards on gender of person, sexual orientation of person, and LGBTQ2+ status of person see Consulting Canadians landing page and StatCan Plus article.
Gender
Statistics Canada initially released new sex at birth and gender variables and classifications on April 13, 2018. Prior to the 2021 Census – in which the gender question was asked for the first time, and the 'at birth' precision was added to 'sex'– Statistics Canada reviewed the gender standard to ensure its relevance. Other engagement activities including a targeted expert consultation supplemented this public consultation in order to update the gender standard.
The updated sex at birth and gender standards were released on October 1, 2021. Among other changes, the definition of gender, the usage sections and the comparison to relevant internationally recognized standards were expanded. In addition, some category names and definitions in the classifications were updated.
Sexual orientation and LGBTQ2+ population
Statistics Canada has been collecting information about sexual orientation since 2003. The variable 'sexual orientation of person' used in the consultation included proposed classifications for the main components of sexual orientation - sexual identity, sexual attraction, and sexual behaviour - which could be measured separately.
The new sex at birth and gender standards have allowed for a more nuanced understanding of sexual orientation and the ability to collect data on the full LGBTQ2+ population. The creation of standards on sexual orientation and LGBTQ2+ status of person will establish a framework to address information gaps on sexual and gender diversity in Canada.
The purpose of the consultation was to ask data producers and users; representatives of civil society organizations; government bodies at the federal, provincial and local levels; academics and researchers; and all other interested parties, including the general public, to submit feedback regarding the proposed updates to the standard for gender and the new standards for sexual orientation and LGBTQ2+ status.
The consultation was conducted electronically and publicized through public announcements that described the proposed updates to the standard for gender of person, and proposed new standards for sexual orientation of person and LGBTQ2+ status of person. The announcements also listed the types of inputs sought, provided a timeline for the consultation and gave contact information for interested parties to make submissions and contact Statistics Canada with questions and comments.
Announcements were disseminated through the Statistics Canada's website and social media. In addition, stakeholders and partners, including civil society organizations, as well as a number of researchers in the field of gender and sexual diversity and gender studies, were invited by email to participate and encouraged to share the consultation invitation with others within their network.
Interested parties were invited to submit written proposals to Statistics Canada. The official consultation period started on February 2, 2021 and closed on March 12, 2021. In addition to the public consultation, virtual meetings were organized with key stakeholders and researchers to gather their feedback.
Statistics Canada received 205 responses by email in both official languages from a range of individuals and organizations:
The consultation also included a number of follow up discussions with academics and subject matter experts.
Statistics Canada is committed to respecting the privacy of consultation participants. All personal information created, held or collected by the Agency is protected by the Privacy Act. As such, the identity of organizations, individuals and academics who participated in the consultation process are kept confidential.
The consultation materials presented an updated definition of gender. In this update, gender was defined as "a person's social or personal identity as a man, woman or non-binary person (a person who is not exclusively male or female)." This definition included the following concepts:
The proposed definition stated that a person's current gender may differ from the sex they were assigned at birth (male or female) and that a person's gender may change over time.
Some of the most consistent feedback received regarding the English version of the gender definition was related to the incongruent use of the biological terms 'male' and 'female'. Respondents also commented that the gender standard conflated sex and gender. To this end, a number of suggestions were received regarding terminology. These included suggestions for the use of 'male', 'female' (or 'intersex') when referring to the biological characteristics of sex, and 'men/boy', 'women/girl', 'transgender', 'cisgender' and 'non-binary' when referring to gender identities. Suggestions were received for the use of gender terminology in the non-binary definition, replacing 'male' or 'female' with 'man' or 'woman'.
Input was received providing suggested modifications to the definitions of gender identity and expression. Feedback was also received on the sex at birth variable which reflected differing perspectives. Some respondents suggested that more emphasis be put on sex assigned at birth, while others suggested that sex is not assigned at birth, but rather observed and reported, and recommended using other terminology.
The proposed usage section included the following explanation, among other content:
The variable 'Gender of person' and the 'Classification of gender' are expected to be used by default in most social statistics programs at Statistics Canada. The variable 'Sex of person' and the 'Classification of sex' are to be used in conjunction with the variable 'Gender of person' and the 'Classification of gender', where information on sex at birth is needed.
While comments specifically referencing the gender usage section were limited, some overarching feedback was received that expressed disagreement with the overall concept of gender identity and communicated concerns about self-identification into protected groups. Some respondents were not supportive of the introduction of the gender variable by default at Statistics Canada and argued that collecting data on gender, rather than sex, could disrupt the historical comparability of the data and result in a loss of informationFootnote 3.
The proposed Classification of gender contained three categories: male gender; female gender; and non-binary gender. The 'non-binary' gender category of the classification is intended to capture relevant write-in responses to the gender question where a respondent indicates being neither exclusively 'man' nor 'woman'.
A few respondents suggested that the classification contain additional categories, such as a Two-Spirit category, with the recommendation that the response option only be available to Indigenous respondents when asked on surveys.
Feedback received regarding the English version of the classifications of gender and transgender status was similar to the comments mentioned above regarding the use of the biological terms 'male' and 'female' in the proposed definitions, with 'man' and 'woman' as suggested replacements.
Comments regarding the reference to 'current gender' (e.g., "This category includes persons whose current gender was reported as male") were received, which suggested the removal of the term 'current'. Similar comments were made regarding the use of the word 'current' in the Classification of transgender status.
Consultations sought input on a classification consisting of the following two broader categories with their respective subcategories (along with their definitions, not presented here):
Respondents suggested the creation of a third, standalone category, 'Non-binary person', rather than being included as a sub-category of 'Transgender person'.
Respondents also provided feedback regarding the terminology. A few commented that the terms 'trans' and 'transgender' are not necessarily interchangeable, while others suggested replacing the term 'cisgender' with 'non-transgender'. A few respondents suggested using the term 'gender modality' as the name of the classification; for example, the Classification of transgender status could be called the Classification of gender modality.
In the proposed standard, sexual orientation was presented as a multidimensional concept defined as an umbrella term that includes a person's sexual identity, sexual attraction and sexual behaviour. Sexual identity refers to how a person perceives their sexuality (e.g., lesbian, straight, bisexual), sexual attraction refers to whom a person finds sexually appealing, and sexual behaviour refers to with whom a person engages in sexual activity. A person's sexual orientation may change over time.
Input was supportive of sexual orientation being a multidimensional concept. Some minor changes were suggested for how to define sexual orientation as well as its different components. Feedback was received in favour of using the term 'sexual orientation' rather than 'sexual identity'. It was also suggested that the definition of sexual orientation should include the concept of emotional attraction.
Feedback regarding usage mainly consisted of the need for transparency around the rationale for collecting data on sexual orientation, ensuring data are only collected as needed. While the consultation did not specifically focus on the minimum age for responding to sexual orientation question, a few organizations and academics provided input on the proposed minimum age of 15. They noted the value of having data on youth who are LGB+ (lesbian, gay, bisexual or of a sexual orientation other than heterosexual), and felt that a rationale should be provided for requiring a minimum age for asking questions about sexual orientation. Similarly, it was suggested that the minimum age to collect data on sexual orientation should be lower than 15 or that a minimum age may not be needed at all.
Consultations sought input on the Classification of sexual identity which included proposed categories along with their definitions. The classification included: 'heterosexual or straight'; 'gay or lesbian'; 'bisexual'; 'pansexual'; 'asexual'; 'queer'; and 'Two-Spirit'. Some respondents suggested including more categories, while others thought the classification should include fewer categories.
It was also pointed out that some of the proposed categories were not mutually exclusive and that this should be addressed (e.g., a person could be both Two-Spirit and bisexual, or asexual and gay, or queer and lesbian). In addition to not being a mutually exclusive category, 'queer' saw some support, but some respondents also suggested to avoid this term loaded with political history and potential derogatory interpretation. Input was also received that the 'Two-Spirit' category was a distinct concept requiring a separate measure only made available to Indigenous respondents.
In addition, feedback was received suggesting that the proposed definitions of different sexual orientations conflated sex and gender by referring to attraction based on sex and/or gender. Some respondents felt that the definition of sexual orientation should solely be based on sex. Other input suggested that sexual orientation definitions include being attracted to a person's gender expression, along with their sex and gender.
Feedback from different types of respondents (i.e., individuals, academics, and organizations) recommended combining the 'bisexual' and 'pansexual' categories, as these terms may overlap and be used interchangeably, making the two categories not mutually exclusive. It was pointed out in some comments that responses may be influenced by whether a person conceptualizes sex/gender as binary or not.
The proposed Classification of sexual identity also included higher levels of aggregation, including category groupings 'heterosexual or straight' and 'minority sexual identity'. Some of the most consistent feedback was that the 'minority sexual identity' category carried a negative connotation and that it was inappropriate. Other feedback suggested that different sexual identities should not be aggregated together.
The proposed Classification of sexual attraction was presented in two versions, each including a number of categories for respondents to identify their sexual attraction. One version measured attraction in reference to the respondent's own gender, without specifying the gender or genders of persons that they are attracted to (e.g., 'person only attracted to person of a different gender'). The other version specified the gender or genders of persons to which the respondent is attracted to (e.g., 'person only attracted to persons of male gender'). Each version also included categories for people who are 'equally attracted' to more than one gender, as well as for people who do not experience sexual attraction or who are unsure of their sexual attraction.
While feedback on which version was preferable was very limited, one of the key issues identified in responses was that both versions included too much detail or that they were too complicated. Others argued that sexual attraction should be defined on the basis of sex rather than gender. While both versions included a category for people who do not experience sexual attraction, some comments suggested that the classification should be more inclusive of people with little or no sexual attraction (i.e., people on the asexual spectrum). It was also suggested to re-name the 'unsure' category to 'questioning'.
Overall, this classification elicited stronger reactions than the other classifications. Some feedback indicated understanding the need to refer to the concept of sex rather than gender in the context of sexual behaviour. However, many comments expressed surprise or confusion that sex rather than gender terminology was used in the proposed sexual behaviour classification, which differed from the proposed sexual identity and sexual attraction classifications which used gender terminology. Some suggested that the purpose of the Classification of sexual behaviour was unclear, and proposed that the classification provide some base standard definitions of 'sexual activity'. Other input suggested to shift the focus away from the sex of the sexual partners towards specific acts.
A significant amount of feedback from different sources (i.e., organizations, individuals and academics) noted that intersex people were only included as partners in the Classification of sexual behaviour and that there was not a specific category for intersex respondents. Some input also indicated that no definition of intersex was provided.
Some respondents suggested that the number of sexual partners should be included within the sexual behaviour dimension. Finally, it was recommended not to refer to 'men who have sex with men' in the classification as the term may have a negative connotation to some people.
Statistics Canada is committed to supporting disaggregated data analysis in order to highlight the experiences of specific segments of the population. Recognizing that sample size may be an issue for small populations, the consultation proposed an aggregate LGBTQ2+ standard to establish a consistent approach to combining data on gender identity and sexual orientation. Input was sought on the proposed definition of LGBTQ2+ status as well as the choice of acronym. The proposed definition was that "LGBTQ2+ status refers to whether or not a person is lesbian, gay, bisexual, transgender, queer, Two-Spirit, or another non-binary gender or minority sexual identity." Feedback received focused on the acronym rather than the proposed definition. The majority of feedback proposed to move the '2' referring to Two-Spirit people at the beginning of the acronym to acknowledge Indigenous people in the context of reconciliation.
Feedback was sought on the proposed Classification of LGBTQ2+ status as two distinct categories (i.e., 'LGBTQ2+ person' and 'non-LGBTQ2+ person' ('heterosexual and cisgender person') as well as their definitions. Some feedback argued against aggregating diverse populations under one umbrella category as these groups have different experiences and are not homogenous in their characteristics. However, others indicated that this approach was a useful way to analyze complex issues experienced by the LGBTQ2+ population as a whole.
Statistics Canada has completed the review process for the updated gender standard and the new sexual orientation standard. The updated gender standard was released on October 1, 2021. All of the comments received during this consultation and other engagements activities were taken into account, and many are reflected in this updated standard.
The new sexual orientation standard was released on August 16, 2023. The public consultation summarized in this What We Heard report was one of four phases that informed the development of the sexual orientation standard. In addition to the public consultation, Statistics Canada undertook a targeted expert consultation, focus groups, and a testing phase which consisted of one-on-one interviews.
The focus groups and testing were conducted in English and French and engaged diverse participants from urban and rural communities in different regions across the country. Participants included LGBTQ2+ and non-LGBTQ2+ individuals from a range of ages, genders and socio-economic status groups. Focus groups and testing also engaged Indigenous Two-Spirit participants, as well as immigrant and racialized participants.
