Bibliographic record
Abstract
À travers le développement spectaculaire de systèmes algorithmiques capables de collecter, d’analyser, de traiter des quantités massives de données, l’ère des Big data semble avoir conféré à l’homme un nouvel outil de prédiction lui permettant d’optimiser les processus décisionnels, d’anticiper les risques et de gouverner les conduites. Les applications, développées notamment dans le domaine de la sécurité, du marketing ou du divertissement, intégrant des algorithmes auto-apprenants, rendent désormais possibles la prédiction et surtout la préemption du futur. Dans cette contribution, nous souhaitons interroger le pouvoir prédictif des algorithmes, à la lumière des pratiques divinatoires de l’Antiquité gréco-latine. À cette fin, nous déployons un double questionnement, à la fois épistémologique et ontologique, en nous inspirant du traité de Cicéron sur la divination : que sont les algorithmes, et quelle est la singularité de leur logique corrélative ? Que nous font voir les algorithmes et quelles sont les nouvelles modalités de production du savoir qu’ils impliquent ?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".