Éduquer au risque d’exclure ? Le cas du projet d’alimentation durable Tast’in Fives
Bibliographic record
Abstract
De plus en plus de villes se lancent dans des projets d’alimentation durable. Or la promotion d’une alimentation dite saine et durable, dont les principes et les pratiques correspondent à l’éthos culinaire des classes moyennes et/ou dominantes, peut parfois servir de cheval de Troie à des processus croissants de gentrification urbaine guidés par un substrat hygiéniste. On assiste alors à une inculcation des conduites qui, à travers la double injonction du manger sain et du devenir mince, utilise une « moralisation douce » qui tend à exclure les populations ne s’y conformant pas ou s’y refusant. C’est aux ressorts de cette inculcation des conduites et aux étiquetages qu’elle produit, mais aussi aux résistances qu’elle engendre, que nous proposons de nous intéresser dans cet article tiré d’une recherche-action sur le projet urbain Tast’in Fives (TIF), mis en oeuvre par la mairie de Lille et financé par l’Union européenne. Notre enquête se base sur de nombreuses observations participantes menées au sein d’ateliers de cuisine organisés dans le cadre de ce projet, ainsi que 50 entretiens approfondis auprès des organisateurs de ces ateliers et de leurs publics.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".