Willingness to pay for credence attributes associated with agri‐food products—Evidence from Canada
Bibliographic record
Abstract
Abstract Credence attributes such as environmental impact, origin, fairness/unfairness, and food safety/health are not available with certainty prior to or at the time of the consumer purchase decision. This creates a problem of imperfect or asymmetric information, leading to suboptimal supply and demand for products with these desirable attributes. Using a representative sample of 2001 Canadian consumers, we adopt, within an attribute‐based decision‐making framework, the asymptotically efficient double‐bounded stated preference approach, to estimate Canadian consumers' willingness to pay for origin, fairness, environmental impact, and food safety attributes associated with pork chops and fresh apples. We find that, on average, consumers are willing to pay significantly more for pork chops and fresh apples that are farmers‐advantaged, sourced from their own province, grown or raised under a production system designed to be environmentally sustainable, and chemical‐free. However, these findings differ significantly by the province of origin, gender, age, and income of the respondents, as well as by product type and attributes being valued.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".