Risk management and precaution: insights on the cautious use of evidence.
Bibliographic record
Abstract
Risk management, done well, should be inherently precautionary. Adopting an appropriate degree of precaution with respect to feared health and environmental hazards is fundamental to risk management. The real problem is in deciding how precautionary to be in the face of inevitable uncertainties, demanding that we understand the equally inevitable false positives and false negatives from screening evidence. We consider a framework for detection and judgment of evidence of well-characterized hazards, using the concepts of sensitivity, specificity, positive predictive value, and negative predictive value that are well established for medical diagnosis. Our confidence in predicting the likelihood of a true danger inevitably will be poor for rare hazards because of the predominance of false positives; failing to detect a true danger is less likely because false negatives must be rarer than the danger itself. Because most controversial environmental hazards arise infrequently, this truth poses a dilemma for risk management.
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 | 0.256 | 0.421 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.014 | 0.006 |
| Science and technology studies | 0.004 | 0.060 |
| Scholarly communication | 0.022 | 0.026 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.021 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 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".