Policy Forum: Tax Policy for Strategic Advantage—Building Canada's Human Capital Edge in the AI and Tariff Economy
Bibliographic record
Abstract
The Canadian job market faces a significant transition as a result of the advancement of artificial intelligence (AI) technology and a new approach to tariffs and trade by the US administration. Without meaningful change in domestic policy, an increase in unemployment rates may have a negative impact on Canada's economy, communities, and the health and well-being of individuals. In this article, we review prior studies to develop a framework that allows us to map the probable impacts of unemployment levels on economies, communities, and individual wellness outcomes. We analyze the potential impact of tariffs and AI adoption on the Canadian job market between 2025 and 2030 using scenario analysis, and find that the most likely outcome is an unemployment rate peaking above 9 percent. Addressing this challenge requires an integrated approach across government policies, including tax policy, to strengthen the economic pillars supporting business enterprises, to advance the development of skills and training, and to drive the creation of enterprise value by employing Canadian talent. We identify critical success factors for corporate tax incentives, suggest prospective policies, and recommend bold policy changes to elevate investment in human capital and help Canada to thrive in an era of AI and tariffs.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".