Bibliographic record
Abstract
In Chapter 5, we explain how a stated preference method, contingent valuation, is used to estimate the value of statistical dog life (VSDL). This shadow price is useful in cost-benefit analyses, included in regulatory impact analyses of rules that affect canine mortality risk. We explain how economists estimate the value of statistical life (VSL) and how to interpret it. We motivate the value of the VSDL with the melamine pet food disaster, which killed many dogs and cats and resulted in the Food and Drug Administration being given more authority to regulate pet food. The VSDL was estimated using a survey experiment presented to dog owners in a national survey. Respondents were asked about their willingness to pay for a reduction in the mortality risk that their dogs face from canine influenza. We describe the construction of the survey instrument and the experimental design, and then demonstrate how the statistical analyses of the resulting data can reduce behavioral biases that might reduce the validity of the results. We conclude by considering how the VSDL may be extended beyond regulatory analysis to tort and divorce law and public policy more generally.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.058 | 0.016 |
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".