THE RATIONAL IMPERATIVE OF PROFESSIONAL RISK MANAGEMENT
Bibliographic record
Abstract
The conundrum of modern post-industrial societies is extraordinary level of overall social well-being punctuated by concerns over multiple risks to which our management responses are ill-adapted.Risks are mostly foreseen but uncertain and often dreadful, but some risks are unforeseen in their likelihood and can have disastrous consequences.The public's trust in the capabilities of professionals to manage the risks is often a matter of contention but, in our view, the primary obligation of risk regulators and professional risk managers is to make paramount the duty to serve the public interest.Risks must therefore be assessed scientifically and quantified: "Numbers, not adjectives" must guide decisions.Relative valuation of what is at risk and what can be sacrificed to reduce the risk is fundamental.Societal preferences about longevity vs. prosperity is expressed by a social indicator, the Life Quality Index LQI.The LQI reflects fundamental human life values and allows explicit balancing of risk with life benefits.It is derived from the economics of human welfare to give a clear guide to risk evaluation.We summarize the derivation of the LQI and derive a paradigm for consistent management of risks in all areas of public exposure to risk.The literature gives many examples that have been implemented in structural engineering and disaster management.It is concluded that the LQI together with the marginal life saving principle provides a reliable and rational approach to quantify, judge and manage public risks with transparency.
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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 | 0.072 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.078 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".