Canada’s high-stakes artificial intelligence gamble: innovation policy, techno-nationalism and the political economy of jobs
Bibliographic record
Abstract
Amid escalating geopolitical tensions and geoeconomic uncertainties, the discourse of artificial intelligence (AI)-driven growth has become tightly interwoven with new narratives of Canadian economic security and sovereignty. At the heart of this agenda lies AI adoption and commercialisation, positioned as central to national competitiveness and future prosperity. This article is part of the AI Policy Observatory for the World of Work (AIPOWW) Symposium for Global Political Economy, which offers a critical political economy analysis of Canada’s evolving AI landscape and gamble. We question the disjuncture between an emerging techno-nationalist narrative and the continuing promise of good jobs. By examining how AI in Canada has been 1) developed, 2) regulated and 3) governed through the lens of nation-building economic aspirations and innovation policy, we argue that the narrative of technological inevitability and the promise of shared prosperity and high-paying jobs remain largely unfulfilled. Considering the failed passage of the AI and Data Act (AIDA) ahead of the 2025 Canadian election, we argue that this regulatory gap creates a pivotal opportunity to orient Canada’s AI strategy beyond an emerging techno-nationalist innovation policy but also as a public good for broad-based prosperity. This shift calls for embedding accountability mechanisms, greater labour participation and public interest by re-centring rights and politics in the pursuit of safer, more secure and responsible AI for everyone.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.026 | 0.020 |
| Scholarly communication | 0.017 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".