Bibliographic record
Abstract
Emerging technologies, such as artificial intelligence and advanced robotics, have the potential to fundamentally change our work and daily lives. In recent years, the understanding of how these technological trends will impact employment has been at the forefront of many recent public debates. Each week there seem to be more and more articles being released about how “robots are taking our jobs.” For the most part, this rich discussion has been driven by the work of many prominent academics and researchers. Unsurprisingly, there are many competing viewpoints. Some argue that disruptive technology will be the driving force behind massive unemployment. Others posit that any potential job loss will likely be offset by productivity increases and employment growth. Despite the extensive literature, this discussion is largely taking place without the use of Canadian data. Although, we know that Canadians are not immune from the effects of automation, and that technological trends will likely have enormous implications for many Canadian industries. But the gap in Canadian-specific knowledge often means that we lack the tools to understand the impact of automation within our own borders. This limits our ability to begin to plan for potential disruption. We therefore felt that it would be useful to apply the findings from the existing literature to the Canadian workforce. To do so, we used methodologies both from both Oxford professors Carl Benedikt Frey and Michael A. Osborne and from management consulting firm McKinsey & Company, which have been employed in other jurisdictions, and applied them both to Canadian data for the first time. It is our goal to help Canadians better understand the effects that automation can have on our labour force. Overall we found that nearly 42 percent of the Canadian labour force is at a high risk of being affected by automation in the next decade or two. Individuals in these occupations earn less and are less educated than the rest of the Canadian labour force. While the literature suggests that these occupations may not necessarily be lost, we also discovered that major job restructuring will likely occur as a result of new technology. Using a different methodology, we found that nearly 42 percent of the tasks that Canadians are currently paid to do can be automated using existing technology. But the data does not paint an entirely negative picture. Using the Canadian Occupation Projection System (COPS), we found that the occupations with the lowest risk of being affected by automation are projected to produce nearly 712,000 net new jobs between 2014 and 2024. As with any type of forecasting exercise, there are always going to be uncertainties associated with the predictions. However, we do hope that this study provides a tool to help guide future decision-making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".