Bibliographic record
Abstract
En France, en septembre 1997, la publication d'Impostures intellectuelles est à l'origine d'une Affaire Sokal qui a envahi l'espace public par l'intermédiaire des quotidiens nationaux et des revues de vulgarisation scientifique.Outre-manche, les Guerres de la science 1 Que tous les lecteurs attentifs de cet article soient chaleureusement remerciés, en particulier Julien Aliquot, Sophie Aliquot-Suengas, Benoît de l'Estoile, Nathalie Montel, Sophie Roux, Marion Thomas et John Tresch.Je suis également très reconnaissante à David Edge, Steve Fuller et Simon Schaffer, d'avoir eu l'obligeance de répondre à mes questions.Cet article a été écrit pour l'essentiel en novembre 2002 et révisé au printemps 2004.Il n'a pas pris en compte la résurgence du débat en février 2004 autour de l'essai de Gabriel Stolzenberg, « Kinder, Gentler Science Wars » (Social Studies of Science, vol.34, n°1).Une version réduite de cet article a été publiée par Genèses, histoire, sciences sociales, sous le titre « En attendant que le porridge refroidisse… La réponse de SSS aux sciences wars », mars 2005, 58, p. 113-131.Pour le rôle de l'affaire Sokal comme révélateur des cultural studies, voir Cusset, French Theory, notamment p. 12 sqq.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.029 | 0.008 |
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".