Entretien avec Peter Galison : Comment établir des ponts entre les sciences pures et les autres disciplines ?
Bibliographic record
Abstract
Les recherches de Peter Galison l’ont mene a composer simultanement avec plusieurs groupes distincts. Que ce soit par son affiliation a la physique et a l’histoire des sciences, en passant par ses travaux sur la materialite, ses documentaires sur des enjeux publics, il vise a etablir un dialogue interdisciplinaire entre les acteurs. C’est en etudiant les interactions entre ingenieurs, physiciens theoriciens et physiciens experimentateurs lors de la conception de radars et de detecteurs de particules que Peter Galison a developpe le concept de zone d’echanges, lieu ou des groupes peuvent se comprendre meme s’ils n’ont pas les memes conceptions. Voici un entretien avec Peter Galison sur ces themes : les frontieres disciplinaires, les representations visuelles et les zones d’echanges.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".