Le récit statistique de l’exclusion
Bibliographic record
Abstract
Le propos de cet article est de montrer comment, à partir d'une formulation incertaine de la question sociale, la statistique de l'exclusion parvient — en dépit d'impossibilités et d'approximations réelles — à construire et à dégager un ensemble de significations propres. Les diagnostics locaux — genre consacré en la matière — offrent en effet une lecture tangible de l'actualité sociale en proposant un dénombrement des situations types associées à l'exclusion, des acteurs dits «exclus», des lieux où s'exerce ce phénomène et des actions susceptibles de l'endiguer. Mais le caractère de vraisemblance de ces diagnostics, une fois déconstruits, se dissipe et laisse apparaître des procédés narratifs qu'il convient d'interroger. L'incapacité d'une telle statistique à appréhender les accidents de trajectoire ou les changements profonds de positions dans la structure sociale oblige à se demander si de telles estimations chiffrées ne manquent pas tout bonnement leur objet.
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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 | 0.017 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".