Bibliographic record
Abstract
Dans la seconde moitié du 20 e siècle, une partie importante des recherches en sémiologie et en sémiotique portait essentiellement sur des considérations linguistiques. Les études sur l’alimentation, notamment sous l’impulsion de Barthes et de Lévi-Strauss, ont non seulement permis d’étendre la sémiotique à d’autres activités, capacités, ou objets culturels humains, elles ont également participé à la naissance des food studies comme un champ disciplinaire autonome. Je propose de revisiter ces études et de sortir du cadre anthropocentrique qu’elles opérationnalisent afin de jeter une nouvelle lumière sur la sémiotique alimentaire. Pour ce faire, j’explorerai les effets d’un bon usage de l’anthropomorphisme et ce qu’implique une sensibilité à l’égard des processus de co-construction des humains et de leurs aliments. Une réflexion sur la minorité dans un contexte alimentaire émergera de cet examen.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".