Willingness to Pay for Reduced Risk of Foodborne Illness: A Nonhypothetical Field Experiment
Bibliographic record
Abstract
This paper focuses on estimating willingness to pay for reducing risk of getting foodborne illness using a nonhypothetical field experiment utilizing real food products (i.e., ground beef ), real cash, and actual exchange in a market setting. Respondents were given information about the nature of food irradiation. Single‐bounded and one and one‐half bounded models are developed using dichotomous choice experiments. Our results indicate that individuals are willing to pay for a reduction in the risk of foodborne illness once informed about the nature of food irradiation. Our respondents are willing to pay a premium of about $0.77 for a pound of irradiated ground beef, which is higher than the cost to irradiate the product. Le présent article porte sur l'estimation de la volonté de payer des consommateurs pour diminuer le risque de contracter une maladie d'origine alimentaire. L'étude a été réalisée en effectuant une expérience sur le terrain à l'aide de vrais produits alimentaires (à savoir du bœuf haché), d'argent réel et d'échange réel en situation de marché. Les répondants avaient reçu de l'information sur l'irradiation des aliments. Nous avons mis au point des modèles à une limite et à une limite et demie utilisant la méthode des choix dichotomiques. Nos résultats ont montré que les consommateurs étaient prêts à payer pour diminuer le risque de contracter une maladie d'origine alimentaire, une fois informés sur l'irradiation des aliments. Nos répondants étaient prêts à payer une prime d'environ 0,77$ la livre pour obtenir du bœuf haché irradié, soit une somme supplémentaire supérieure au coût de l'irradiation du produit.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".