For and againstDoes risk homoeostasis theory have implications for road safetyForAgainst
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 …
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it