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Record W4416552843 · doi:10.1093/inteam/vjaf174

Enabling regulatory innovation: precaution, risk, and new approach methodologies

2025· article· en· W4416552843 on OpenAlexaffabout
Paris Jeffcoat, Jaye Ellis, Gordon M. Hickey, Steven Maguire, Niladri Basu

Bibliographic record

VenueIntegrated Environmental Assessment and Management · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicRisk Perception and Management
Canadian institutionsMcGill University
Fundersnot available
KeywordsArgument (complex analysis)Precautionary principlePaceAction (physics)Risk managementEnvironmental policy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.699
Threshold uncertainty score0.634

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.061
GPT teacher head0.360
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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