Accountability, Risk, and the ALARP (As Low As Reasonably Practicable) limit of benefit
Bibliographic record
Abstract
Professionals who assess public life and health risks are obliged to give reasoned account of the regulations they propose. Professional management of risk requires not just probabilistic analysis but also a rational defensible choice of the acceptable risk. The justification should be objective and quantified. The economics of human welfare, expressed through the Life Quality Index, defines net benefit to society of regulations to mitigate such risks. Together with the marginal life-saving cost principle, the Life Quality Index provides a clear limit of benefit to risk reduction. The underlying principles and the Life Quality Index are explained. Some issues of practical application are illustrated by an example of temporary facilities for storage of toxic waste. Effective communications about risk, to authorities in the form of regulatory scorecards and to the public, are stressed as integral components of the risk management process.
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 | 0.054 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.040 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.012 | 0.009 |
| 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".