Est-il bon, est-il méchant? Le rôle du nombre dans le gouvernement de la cité néolibérale
Bibliographic record
Abstract
Comment résoudre la contradiction entre l’ethos du statisticien et la prise en compte des rétroactions, même quand celles-ci lui apparaissent seulement comme de fâcheux obstacles à sa mission, qu’il pense être de « fournir des reflets non biaisés de la réalité » ? Il n’est pas possible d’isoler un moment de la mesure, qui serait indépendant de ses usages, et notamment des conventions qui sont la première étape de la quantification. Il faudrait désenclaver la formation des statisticiens, en la complétant par des éléments d’histoire, de sciences politiques, et de sociologie de la statistique, de l’économétrie, des probabilités, de la comptabilité et de la gestion. Ce programme, inspiré des acquis des sciences studies (Pestre, 2006), pourrait faciliter la prise en compte des outils quantitatifs dans les débats sociaux, sans verser ni dans le rejet a priori, ni dans le respect inconditionnel et naïf devant des « faits incontestables parce que quantifiés ».
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".