Marketing Opportunities for Certified Pork Chops
Bibliographic record
Abstract
U.S. consumers are increasingly concerned about food safety, environmental degradation, and animal welfare at the live animal production stage. In response, meat suppliers are developing food credence certification to secure market access, increase margins, and increase overall demand. The objective of this paper is to characterize the demand and the market potential of a credence certification program for pork in the United States. Information regarding consumer willingness to pay for the conventional and certified products is derived from a latent class random utility model. The willingness to pay estimates are subsequently compared to the costs of implementing the programs at the producer, packing, and retailing stages. One of the findings in this study is that a significant segment of consumers would purchase certified pork at the anticipated marginal cost of certification. Therefore, future studies should consequently focus on the welfare economic implications on consumers and meat suppliers from incomplete adoption of voluntary certification programs on the part of both producers and consumers. Aux États‐Unis, les consommateurs se préoccupent de plus en plus de la sécurité alimentaire, de la dégradation de l'environnement et du bien‐être des animaux au stade de la production. En réponse à ces inquiétudes, les fournisseurs de viande travaillent à l'élaboration de programmes de certification des aliments pour garantir l'accès au marché, accroître les marges ainsi que la demande globale. Le présent article vise à caractériser la demande et le potentiel de marché d'un programme de certification du porc aux États‐Unis. L'information concernant la volonté de payer du consommateur pour des produits classiques et des produits certifiés a été tirée d'un modèle d'utilité aléatoire à structure latente. Les estimations de la volonté de payer ont ensuite été comparées aux coûts de mise en place des programmes aux stades de la production, de l'abattage et de la vente au détail. L'un des résultats de l'étude a montré qu'un nombre important de consommateurs achèterait du porc certifié au coût marginal prévu de la certification. Des études ultérieures devraient donc se pencher sur les répercussions économiques de l'adoption incomplète des programmes de certification volontaires de la part des producteurs et des consommateurs sur le bien‐être des consommateurs et des fournisseurs de viande.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".