Quel prix pour la sécurite alimentaire? Une évaluation contingente suite à la crise européenne de la ≪ vache folle ≫
Bibliographic record
Abstract
L'hypothèse d'une éventuelle contamination humaine par la maladie de Creutzfeldt‐Jakob, par le biais du prion de I'encéphalite spongiforme bovine (ESB), faite en 1996 par le ministre de la santé anglais a étéè l'origine d'une perte de confiance des consommateurs dans la viande de bœuf. Lew comportement en France a étéétudié a partir d'une enquête menée en 1997, auprés d'un échantillon représentatif de 700 personnes résidant dans l'agglomération de Rennes en Bretagne. L'enquête présente aux personnes interrogées un scénario contingent visant èévaluer leur consentement è payer (CAP) afin de disposer d'une viande pour laquelle tout risque de contamination par l'ESB est écarté. Un processus original de révélation des CAP a été utilisé, combinant la méthode du référendum et un système d'enchères. Le CAP moyen obtenu est relativement élevé puisqu'il atteint 22 % du prix initial de la viande. Un modéle micro‐économique visant è expliquer la valeur du CAP du consommateur pour une sécurité alimentaire totale par rapport è l'ESB est alors présenté. De cette formalisation micro‐economique découle une spécification économétrique correspondant au modele multinomial logit ordonnéè seuils connus. Parmi les variables ayant un role determinant dans le choix du CAP des consommateurs figurent notamment la confiance dans les indications géographiques et les labels. After the British Minister of Health admitted a possible link between the BSE prion and the Creutzfeldt‐Jakob disease in 1996, consumers lost confidence in the beef sector. This paper analyses consumers behavior after the BSE crisis in France in 1997, based on a survey using a random sample of 700 people located in the area of Rennes (Bretagne). Particularly, a contingent scenario was proposed to the respondents to elicit their willingness to pay (WTP) to have beef which would not transmit CJD using a mix of the referendum method and the bidding process. The mean WTP reaches 22% and is consistent with results obtained in other studies. A microeconomic model is proposed and an econometric approach based on an ordered logit model is developed. Among the significant variables, it is interesting to note the role of stamps of quality to explains consumers'behavior. Such variables prove the interest of a transparency and tracability policy to restore the consumers confidence.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".