HOW TO MAKE A PUBLIC CHOICE ABOUT THE VALUE OF A STATISTICAL LIFE: THE CASE OF ROAD SAFETY
Bibliographic record
Abstract
Cost-benefit analysts involved in evaluating projects influencing the risk of death and injury have access to a wide group of studies that provide a large range of estimates of the value of a statistical life (VOSL). It is of course a difficult task to pick the right estimate. This paper discusses the potential avenues available to analysts looking for values of a statistical life and of injuries to be used in cost-benefit analyses of Quebec projects involving changes in road safety. Actually, the discussion is conducted in the context of Quebec, but most of it could easily apply to the rest of Canada. First, we discuss the relevance of looking for an original set of estimates involving a new study and the collection of new data. We present many arguments in favour of such a strategy. Second, if the time or the resources necessary to conduct a new study are not available, we offer an analytical framework that allows one to make a choice of estimates (or of a range of estimates) from existing studies. We conclude that a VOSL of 5 million dollars (CAN $, 2000) would be acceptable. Another contribution of this paper is to present, to our knowledge, the most up-to-date survey of studies on the value of a statistical life covering more than 85 papers.
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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 | 0.100 | 0.278 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".