Vulgariser la science pour réaffirmer son rôle de savant. L’Essai philosophique sur les probabilités de Laplace
Bibliographic record
Abstract
Scientifique parmi les plus célèbres de la fin du xviiie siècle et du début du xixe siècle, Pierre-Simon de Laplace se veut un grand vulgarisateur de ses découvertes. Son Essai philosophique sur les probabilités témoigne de l’importance attachée par le savant à ce domaine, que les philosophes, à son époque, considèrent souvent en marge des autres savoirs scientifiques. À travers cet ouvrage, Laplace essaie, d’une part, d’établir sa puissance institutionnelle et auctoriale et, de l’autre, de convaincre aussi bien les savants que les non-scientifiques de la validité de ses théories. Nous nous proposons d’analyser ici les dynamiques de vulgarisation des savoirs mises en oeuvre par Laplace dans cet ouvrage afin de démontrer, notamment au moyen des réflexions portant sur des choix discursifs et linguistiques, que celles-ci n’ont que l’apparence d’une vulgarisation, réaffirmant plutôt les enjeux institutionnels et auctoriaux liés au pouvoir, à l’époque laplacienne.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".