Le partage de nourriture comme « langage d’affection »
Bibliographic record
Abstract
L’article explore les comportements alimentaires d’une personne étudiante non binaire, Gill, qui utilise la préparation et le partage de repas comme vecteur de lien social et langage d’affection. Ces pratiques sont analysées dans le contexte scolaire collégial, caractérisé par des horaires surchargés et des exigences supplémentaires en ce qui a trait au travail scolaire. L’article souligne l’importance des routines alimentaires pour Gill, qui lui permettent de faire face à la désorganisation temporelle, et met en lumière les pratiques singulières qu’iel développe en réponse à l’individualisation des problèmes alimentaires vécus pas plusieurs étudiant·es. L’analyse montre ainsi comment certaines pratiques alimentaires porteuses de sens peuvent contribuer au bien-être individuel et collectif.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".