Regulation of Chemical Risks: Lessons for Reform of the Toxic Substances Control Act from Canada and the European Union
Bibliographic record
Abstract
The purpose of this Article is to compare the regulatory systems in Canada and the EU, and use comparative insights to draw some lessons that may be of interest to U.S. policy makers engaged in TSCA reform. CEPA and REACH are seen by stakeholders as state of the art in chemicals assessment and management, and thus the U.S. may draw useful insights from them. Indeed, the European Union and Canada have each been urging other countries to join in a globalization of the REACH or Canadian programs, respectively. Regardless of what TSCA reformers choose to learn from the Canadian and European experiences, a secondary objective of the Article is to provide comparative information that may be of interest to reformers in Canada, Europe, or other countries and regions where chemical risk management is under consideration for reform. Thus, the Article's long-term value extends beyond the current U.S. debate over TSCA reform. The Article is organized in three Parts. In Part I, we describe the scope of our analysis, our research methods, and our analytical approach. In Parts II and III, we compare CEPA and REACH across two significant dimensions: (1) prioritization of existing chemicals for assessment and regulation; and (2) placement of the burdens to produce data and demonstrate safety of specific chemical uses. We conclude by summarizing the possible lessons for TSCA reform and highlighting some future research needs.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.008 |
| 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".