L’ <i>Ars conjectandi</i> de Jacob Bernoulli et l’objectivité d’une connaissance incertaine
Bibliographic record
Abstract
À la fin du xvii e siècle, le calcul des probabilités ouvre la voie à une mathématisation inédite de la certitude morale, qui conduit à repenser les catégories classiques de la certitude. L’ Ars conjectandi (1713) de Jacob Bernoulli esquisse un nouveau modèle épistémologique autour de la mesure mathématique du probable et une nouvelle manière d’aborder le problème de l’incertitude de la connaissance. L’« art de conjecturer » de Bernoulli pose la probabilité comme une propriété irréductible de certains champs et modalités de connaissance, mais qui peut être objectivée par sa mesure mathématique et ainsi réintégrée positivement au sein de la connaissance, comme une propriété objective. Entre le champ pratique des décisions en vue de l’action et le champ théorique de la philosophie naturelle, l’art de conjecturer de Bernoulli cherche à élaborer un espace épistémologique inédit qui bouscule les délimitations disciplinaires traditionnelles.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".