Valuing Accidents Using Stated Preference Methods
Bibliographic record
Abstract
We consider the value of a statistical life (VSL) at risk in interurban road accidents for car users. We use a stated preference (SP) experiment involving three variables: travel time, toll charge and accident in a medium distance route choice context. Apart from the SP exercise, respondents answered an accident risk perception questionnaire loosely based on the work of Jones-Lee and colleagues. The data was subject to a thorough set of consistency tests, including departures from the linear compensatory hypothesis (i.e. lexicographic and non-linear compensatory individuals). The paper discusses the results of several classes of SP route choice models involving risk. These allow to infer subjective values of time (SVT) and willingness-to-pay (WTP) estimates for reductions in accident risk, which allow to derive VSL for the highway under consideration. The SVT values are compared with values obtained previously in the country in order to check respondents understanding of the SP experiment. Our results indicate that use of transferred values from the USA is not recommended as these are significantly different from our estimates.
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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 | 0.020 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".