Doomed to Fail: Ag-gag Laws and the Canadian Charter
Bibliographic record
Abstract
In late 2019, ag-gag laws began being introduced in Canada. Ag-gag laws are named for their intended effect of gagging activists from exposing the realities of the animal agriculture industry. Animal activists seek to gather and publicly disseminate information using means of bearing witness, undercover investigations, and civil disobedience. Ag-gag laws originated in the US in the 1990s, but saw a revival in the 2010s. In the US, animal law organizations such as the Animal Legal Defense Fund have been successfully challenging the constitutionality of ag-gag laws, with courts in six states finding ag-gag laws to violate the First Amendment right to free speech. Despite the failures of ag-gag laws in US courts, various governments in Canada began introducing ag-gag laws to shield the animal agriculture industry from the growing activism in Canada. In drawing parallels between the US right to free speech and the Canadian Charters s. 2(b) freedom of expression, this thesis argues that Canadian ag-gag laws must also be found to unconstitutionally violate the Charter. To be sure, ag-gag laws suppress important activist expression in a way that cannot be justified in a free and democratic society. This thesis seeks to capture the current picture of ag-gag laws in Canada as of June 2021 in anticipation of the impending Charter challenges by Animal Justice et al.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.035 | 0.045 |
| Scholarly communication | 0.018 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".