Scale Sensitivity and Numerical Skills in Contingent Valuation Surveys of Risk Reduction Policies
Bibliographic record
Abstract
This paper considers data from a 2013 study conducted in Newfoundland about the value of reducing mortality risks due to moose-vehicle collisions. Willingness to pay was modeled using a maximum likelihood routine that allowed factors that determine sensitivity to scale to be modeled as functions of covariates. This allows comparison of sensitivity to scale between groups. A unique aspect of the survey design elicited a near continuous distribution of risk reduction levels which allowed for a more in depth analysis of scale sensitivity. There are several key ndings from this paper that support previous research ndings in assessing scale sensitivity. It was found that previous experience with the risk involved was a signicant factor in determining sensitivity to scale. Additionally, we nd support for the hypothesis that cognitive ability is also a determinant of scale sensitivity. Individuals with better cognitive skills are more likely to show sensitivity to scale in both the weak and strong form tests. It was found in the weak form test that sensitivity to scale is less likely to be present at lower risk reduction levels. Additionally, own death risk perception was modeled using an OLS regression and the level of math score (on a scale of 0 to 4 correct questions) was modeled using an ordered logit model.
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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 | 0.031 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".