Nothing to hide: How governments justify the adoption of ag‐gag laws
Bibliographic record
Abstract
Mainstream practices for producing meat, eggs, and dairy raise numerous concerns regarding public health, animal welfare, and environmental integrity. However, governments worldwide have expanded anti-whistleblower legislation that constrains informed public debate. Since 2019, several Canadian provinces have adopted so-called "ag-gag" laws designed to prevent hidden-camera investigations on farms and meat processing facilities. How do governments across Canada justify ag-gag laws as serving the public interest? To what extent do agricultural industry interests shape government adoption of ag-gag laws? Using Freedom of Information requests and debate records from provincial legislatures, we find that biosecurity is the most prominent justification for ag-gag laws, and that governments exhibit a close, collaborative relationship with industry actors. This case demonstrates that when it comes to contested sites of capital accumulation, governments are drawing on new spatial-legal tools to protect the status quo interests of private industry by dissuading dissent, debate, and public scrutiny.
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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 | 0.033 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.019 | 0.044 |
| Scholarly communication | 0.019 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".