Comprendre l’hétérogénéité sociale pour faire valoir la diversité1
Bibliographic record
Abstract
Alors que le vocable « diversité » gagne en popularité et que l’on dénonce avec lui la « norme mythique » d’un sujet social universel, on observe une confusion et un glissement sémantique entre les termes « diversité » et « hétérogénéité sociale intragroupe ». Les auteures proposent une comparaison différenciée de ces deux syntagmes et de leur valeur heuristique pour penser l’émancipation. Elles présentent une série de propositions analytiques conduisant à la nécessaire distinction entre la nature normative de la notion de diversité et le fait social que constitue l’hétérogénéité intragroupe – pour tout groupe social. Ce faisant, elles se penchent sur la complexité du concept de résistance et rappellent la part ternaire de tout rapport de domination.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".