Crafting an Intersectional Response to Bill C-27 for the Standing Committee on Industry and Technology
Bibliographic record
Abstract
Background: In the fall of 2023, a group of scholars prepared a brief to respond intersectionally to Bill C-27 after its second reading and study by the Standing Committee on Industry and Technology. Updates to Canada’s private sector privacy law and regulation of artificial intelligence were important elements proposed in Bill C-27. Analysis: This article reflects upon five significant issues related to our brief and intersectionality. Conclusion and implications: Our reflections contribute to a greater understanding of the critical/administrative dichotomy in communications scholarship, and to knowledge about how to apply an intersectional approach to privacy and AI policy in Canada.
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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 | 0.043 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.053 | 0.022 |
| Scholarly communication | 0.022 | 0.007 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.031 | 0.037 |
| Insufficient payload (model declined to judge) | 0.009 | 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".