Lobbyist Framing of Artificial Intelligence in Canada
Bibliographic record
Abstract
Our research looks at the role lobbying plays in influencing artificial intelligence (AI) policy in Canada, especially during the agenda-setting and problem definition stages. Our research uses Critical Discourse Analysis (CDA) and policy agenda-setting theory to analyze federal parliamentary committee hearings and to uncover the underlying power dynamics of these stakeholder engagement venues. This research highlights a significant lack of representation of marginalized voices in this public forum, creating additional social exclusion for these groups in AI policymaking. As a result, policy recommendations stemming from these meetings did not properly account for AI-related risks felt by marginalized communities. Our findings show that lobbying groups use specific discursive strategies to further their self-interest, and that negativity bias strongly influences policymakers, prioritizing AI-related risks over benefits. Our findings contribute to the literature on social inclusion, lobbying, and AI governance. Thus, we emphasize the need for more equitable representation in AI policy discussions.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.033 | 0.014 |
| Scholarly communication | 0.018 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".