Mesure des propensions individuelles à payer pour les aliments fonctionnels : Une approche expérimentale auprès de consommateurs français
Bibliographic record
Abstract
Cet article présente les résultats de recherches en économie expérimentale visant à mesurer les propensions à payer d'un échantillon de consommateurs français pour des yaourts fonctionnels. Nous mesurons l'impact de l'information concernant les caractéristiques fonctionnelles des produits, ainsi que l'impact de l'intensité fonctionnelle des produits sur la participation au marché et sur les propensions individuelles à payer. Les résultats indiquent que la participation au marché diminue avec l'intensité fonctionnelle et que plus de 35 % des acheteurs, s'ils étaient mieux informés, choisiraient de ne pas consommer de produits à forte intensité fonctionnelle. Parmi les participants au marché, nous observons que l'information et l'intensité fonctionnelle influencent significativement les propensions à payer.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".