Bibliographic record
Abstract
The R-101 is as safe as a house, except for the millionth chance. Lord Thomson, Secretary of State for Air, shortly before boarding the airship headed to India on its first flight, October 4, 1930 If you risk nothing, then you risk everything. Geena Davis What is risk? Its analysis as decision theory It is commonplace to make statements such as ‘life involves risk’, or ‘one cannot exist without facing risk’, or ‘even if one stays in bed, there is still some risk – for example, a meteorite could fall on one, or one could fall out and sustain a fatal injury’, and so on. The Oxford dictionary defines risk as a ‘hazard, chance of … bad consequences, loss, …’; Webster's definition is similar: ‘the chance of injury, damage or loss’ or ‘a dangerous chance’. Two aspects thrust themselves forward: first, chance, and second, the unwanted consequences involved in risk. We have taken pains to emphasize that decision-making involves two aspects: probabilities (chance), and utilities (consequences). Protection of human life, property, and the environment are fundamental objectives in engineering projects. This involves the reduction of risk to an acceptable level. The word ‘safety’ conveys this overall objective. In Chapter 4, aversion to risk was analysed in some detail. The fundamental idea was incorporated in the utility function. We prompt recollection of the concept by using an anecdote based on a suggestion of a colleague. A person is abducted by a doctor who is also a fanatical decision-theorist.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".