Produits de terroir versus produits locaux : une perception différenciée selon deux cultures alimentaires française et québécoise
Bibliographic record
Abstract
Cet article explore la perception des produits de terroir et locaux à travers une étude qualitative interculturelle effectuée auprès d’un groupe de 30 consommateurs en France et au Québec. Les résultats mettent en avant cinq dimensions expliquant les perceptions différenciées dans les deux cultures : fonctionnelle, identitaire, résistance, émotionnelle et relationnelle. Au-delà de l’intérêt managérial pour les producteurs de bien connaître leurs cibles en identifiant leurs significations culturelles, les résultats de cette étude contribuent à la compréhension des techniques de promotion en adaptant le discours et les offres en matière de produits de terroir/locaux français aux caractéristiques des différentes cultures alimentaires au niveau international.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".