Testing Toxicity: Proof and Precaution in Canada's Chemicals Management Plan
Bibliographic record
Abstract
This article explores questions of proof and precaution in the context of Canada's new Chemicals Management Plan. That plan includes a bold initiative known as the ‘Challenge’, under which the government has identified 200 high priority chemicals for which it is ‘predisposed’ to a finding of toxicity. The presumption will operate unless the challenged stakeholders submit ‘information’ sufficient to rebut it. Through comparison with the European REACH regulation, this article explores exactly what burdens have been shifted, to whom and why. It also evaluates the significance of this move for the governance of chemicals in Canada and for the health of Canadians. It looks specifically at the case of Bisphenol A, which was one of the 200 chemicals included in the Challenge, and was recently declared toxic under that process. The Challenge forces us to confront the ‘dilemma of industry data’, which complicates the debate over a shifted burden of proof in the context of chemicals management.
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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 | 0.064 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.020 | 0.036 |
| Scholarly communication | 0.021 | 0.010 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.032 | 0.020 |
| 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".