Assessing the Impact of Generative AI on Canadian Labor Market: An Empirical Approach
Bibliographic record
Abstract
The rapid advancement and integration of Generative AI and Large Language Models (LLMs) into various sectors raise significant concerns about their impact on the labor market. This research assesses the extent to which occupations in Canada are exposed to these technologies. Using data from the Canadian Occupational and Skills Information System (OaSIS) and adapting the methodology of Felton et al. (2018, 2021, 2023), we calculated AI Occupational Exposure (AIOE) scores for 900 occupations. The findings demonstrate a high correlation between Canadian and U.S. occupations in terms of AI exposure, with Pearson and Spearman coefficients of 0.888 and 0.883, respectively. Approximately 45% of the Canadian workforce, or 9.2 million people, are in sectors with high AI exposure, indicating significant potential for job transformation. Notably, roles in management and business-related occupations, which account for over 25% of total employment, show an AI exposure rate of 86% and 88%, respectively. The study highlights the need for upskilling in highly exposed occupations, particularly in management, finance, and applied sciences. While this research addresses an important gap in understanding Generative AI’s impact on the Canadian labor market, it also identifies several limitations, including the lack of detailed ability importance data and confidentiality restrictions on fine-grained employment data. Future research should explore the regional impacts of Generative AI, as well as the effects on various demographic groups.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".