Enabling regulatory innovation: precaution, risk, and new approach methodologies
Bibliographic record
Abstract
The precautionary principle, a risk management tool used to justify deliberations and actions to prevent potential risks, has guided environmental policy development and implementation, both legally and culturally, and its application may play a pivotal role in the formal adoption of New Approach Methodologies (NAMs) in next generation environmental risk assessment. We consider the pace of NAM integration in environmental risk assessment and ask how, and why, a disconnect exists between policy development (where one finds precaution used as an argument for the adoption of NAMs) and policy implementation (where one finds precaution used as an argument against the adoption of NAMs). Reviewing how the precautionary principle is invoked in the Canadian context, we explore how competing interpretations of 'precaution' and 'risk' can be used to justify both regulatory action and inaction, hamstringing regulatory innovation related to the validation and acceptance of NAMs. Clarification among stakeholders of convergent and divergent interpretations and hence application of these concepts in practice is recommended to increase confidence in NAMs, providing a way forward for their incorporation in environmental risk assessment.
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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 | 0.098 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.011 | 0.087 |
| Scholarly communication | 0.023 | 0.015 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".