Bibliographic record
Abstract
On ne peut pas traiter du goût de la même façon dans une période de relative rareté des consommations et dans une situation d’abondance de l’offre, d’impact significatif des médias et d’influence croissante du marketing. C’est pourquoi l’article propose, en s’appuyant sur des exemples pris dans les consommations liées au sport, une analyse critique de la modélisation du goût proposée par P. Bourdieu. En effet, les liens entre habitus, goûts et consommation sont plus incertains. La massification de la consommation et la diversification des expériences sociales ont transformé les modes de construction sociale des goûts. Ce nouveau contexte de consommation invite à débattre de l’explication du goût comme expression d’une culture de classe. Si la culture de masse ne supprime pas les hiérarchies des goûts et les dominations sociales, elle nous invite à les repenser.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".