Ethical Issues in Toxic Chemical Hazard Evaluation, Risk Assessment and Precautionary Communications
Bibliographic record
Abstract
Abstract Questions that are central to the examination of ethical issues in the risk‐assessment process are: Who needs to be protected, against what and how? These questions attract attention to the value‐laden judgement of the risk‐assessment process itself. This is the background upon which potential ethical pitfalls associated with the professional act of risk assessment are examined. Scientific risk assessments should contribute to improving public health rather than to serving pure ideology of interest groups. Toxicological risk assessment is the art of translating basic toxicological and epidemiological information into recommendations for risk‐management decisions. The framing of the risk questions shapes the risk assessment itself and is therefore subjected to the biases and values of those who launch the risk‐management process. Ill‐defined risk questions will result in ill‐assessed risks. Risk questions are, in essence, complex, and attempts to simplify them may severely distort the real questions that need to be addressed. Beyond biomedical aspects, risks to health include social components that are intrinsically part of the risk and should be given appropriate consideration. The controversial precautionary principle can be used as a sound evolutionary approach to risk decision‐making provided it is approached in a consistent, transparent manner.
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 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 | 0.047 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.033 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".