A contingent valuation study of Winnipeg municipal water quality using bounded rationality
Bibliographic record
Abstract
Contingent valuation (CV) is a survey technique used to value environmental goods not traded in markets, such as improvements to air or water quality. Despite its popularity, widespread acceptance of this methodology has been hampered by controversies stemming from numerous behavioral anomalies such as preference reversals, embedding and starting point bias. This thesis argues that these anomalies are better understood using bounded rationality to model behavior, rather than traditional theories of rationality. To prove this, a conceptual framework is developed which explains the various aspects of bounded rationality. This framework is then applied to a literature review of contingent valuation and related studies, and a CV experiment. The contingent valuation experiment uses a research design with techniques designed to both mitigate and observe these anomalies. A "shopping experience" scenario was constructed; a protocol analysis technique called 'retrospective reporting' was used, and attitude questionsin the survey were used to construct indexes for an econometric model. The environmental good valued was an improvement in Winnipeg municipal water quality that would result if a modern treatment plant was built. (Abstract shortened by UMI.)
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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 | 0.003 | 0.010 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".