For and againstDoes risk homoeostasis theory have implications for road safetyForAgainst
Bibliographic record
Abstract
# Does risk homoeostasis theory have implications for road safety {#article-title-2} Risk homoeostasis (also called risk compensation) theory predicts that, as safety features are added to vehicles and roads, drivers tend to increase their exposure to collision risk because they feel better protected. Gerald Wilde provides evidence for it and suggests that it should be used to inform road safety strategies. Leon Robertson and Barry Pless, however, argue that the evidence is deeply flawed and that the theory is little better than an excuse for doing nothing # For {#article-title-3} Anyone wishing to reduce the risk of misfortune on the road to zero can do so by never using the roads, but that person would also miss all the benefits accruing from road travel and thus live a greatly diminished life. Suboptimal risk taking also occurs if a person underestimates or overestimates the danger of a given activity, because that person would either take too much risk or too little for greatest net benefit. A person learns to assess risk by perceiving the outcomes of decisions. Our intuitive assessment of risk is honed by our experience and that of others, sometimes communicated through the mass media. This feedback will thus confirm or correct a person's perception of the size of the four utility factors that determine the optimal (or target) level of risk (see box). #### Theory of risk homoeostasis While some actions entail more danger (probability×magnitude of loss) than others, there is no behaviour without some risk. The challenge, therefore, is to optimise rather than eliminate risk. This optimal, or target, level of risk is that which maximises the overall benefit (probability×amount). Four utility factors determine the target level of risk: The first two factors increase …
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".