Respondent uncertainty in contingent valuation: the case of whale conservation in Newfoundland and Labrador
Bibliographic record
Abstract
In this paper we investigate the issue of respondent uncertainty in contingent valuation studies while estimating the willingness to pay for a whale conservation program o¤ the coasts of Newfoundland and Labrador. We use data from a phone survey administered to a sample (N=614)\nof adult Canadians, proposing a policy consisting of subsidizing and enforcing the use of acoustic devices that would reduce the likelihood that whales become entangled in �shing nets. A follow-up question asked respondents how certain they were about their answer to the main dichotomous-choice question, which allows us to investigate how the treatment of uncertainty a¤ects value measures. A mean willingness to pay of about $81/year per respondent is estimated when accounting for the degree of certainty with which respondents expressed their willingness to pay. We also analyze payment vehicle e¤ects using a split-sample approach whereby some respondents were asked a dichotomous-choice question about a tax contribution while others were asked about a voluntary donation instead.
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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 | 0.018 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".