Breaking the “Toxic Ignorance Cycles” that Hinder New Approach Method (NAM) Acceptance in Environmental Risk Assessment
Bibliographic record
Abstract
The environmental risk assessment community currently faces an existential dilemma. Policy mandates increasingly encourage transformation of the traditional risk assessment paradigm: a shift away from whole animal toxicity testing, toward New Approach Methodologies (NAMs). While NAMs offer a promising new means of (eco)toxicological knowledge production for decision-makers, uncertainty, ignorance, and risk related to their development, validation, and regulatory acceptance remain, limiting their adoption in practice. We offer a new perspective on this challenge, unpacking what uncertainty and ignorance in relation to NAM innovation means, and encouraging actors involved in NAM innovation to critically reflect on how uncertainty is currently used to justify regulatory nondecisions and inaction. We introduce the concept of the toxic ignorance cycle which we conceptualise as a "vicious cycle" of environment, health, and safety knowledge gaps, institutionalized ignorance, nondecision/inaction, and outdated (or unfulfilled) legal mandates for environmental protection. We also explore how toxic ignorance cycles can be broken through actions aimed toward addressing uncertainty, ignorance, and risk, which operationalise the precautionary principle and the Responsible Innovation (RI) framework. Our aim is to encourage precautionary, ethical, and reflective NAM innovation and regulatory adoption.
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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 | 0.187 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.063 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.007 | 0.014 |
| 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".