`Some Strange Things Happening in our Country': Opposing Proposed Changes in Anti-Cruelty Laws in Canada
Bibliographic record
Abstract
Ideologies are most efficient when they operate invisibly to naturalize power relations, yet even the most successful hegemonies are not seamless. Contradictions in our attitudes towards animals are exposed by welfare measures such as anti-cruelty laws. This article examines opposition to proposed amendments to Canada's anti-cruelty legislation. Directed at individual acts of violence, anti-cruelty amendments proposed in Bill C-15B posed no challenge to animal exploitation industries and consisted of only moderately increased penalties for deliberately sadistic actions in non-institutional settings. The amendments were not motivated by ideas from an animal-rights perspective and constituted no fundamental change. Nevertheless, industrial users of animals presented even modest reforms as a direct threat to their interests. This article examines discursive strategies of those who profit from institutionalized abuse and killing of animals and shows that they deliberately exaggerated the amendments' effects and vilified those with genuine and reasonable concerns for the welfare of animals as `radicals', `extremists' and `terrorists'.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.039 | 0.013 |
| Scholarly communication | 0.009 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".