« C’est génétique » : ce que les twin studies font dire aux sciences sociales
Bibliographic record
Abstract
Cet article est une analyse des effets de l’utilisation de la génétique en sciences sociales à travers l’un de ses principaux instruments génériques, les twin studies , méthode qui consiste à quantifier l’influence des facteurs génétiques et environnementaux sur un comportement humain donné en utilisant des jumeaux monozygotes et dizygotes. Conséquence immédiate de la stratégie de générosité intéressée des généticiens analysée par Aaron Panofsky (2014), les twin studies facilitent la recherche interdisciplinaire entre sciences sociales et les collaborations entre sciences sociales et sciences naturelles. De façon notable, le moment de son apparition en sciences sociales est constant à travers quatre disciplines (criminologie, économie, sociologie, science politique). Au-delà de la standardisation scientifique qu’il entraîne, l’instrument générique twin studies prend des formes locales intra-disciplinaires. À l’inverse des généticiens, les chercheurs en sciences sociales ont tendance à mobiliser des versions méthodologiques simplifiées du modèle twin studies , ce qui est concordant avec le concept de « distance sociale » développé par le sociologue des sciences Harry Collins dans son étude de la découverte des ondes gravitationnelles en physique (2010).
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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 | 0.095 | 0.243 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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; 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".