Bibliographic record
Abstract
Les fournisseurs d’IA parlent maintenant des raisonnements réalisés par leurs machines et, devant les résultats exceptionnels de la nouvelle vague dite « générative », les slogans publicitaires semblent enfin prendre vie. Résistent-ils pour autant à l’analyse ? Des intérêts commerciaux bien compris ne chercheraient-ils pas à entretenir une forme de confusion entre leurs intelligences (artificielles) et l’intelligence tout court ? Cet article commence par regrouper quelques caractéristiques saillantes de cette dernière avant de rappeler ce qui fait l’IA moderne et débouche sur les grands modèles de langage, leurs verbiages stochastiques et le verbiage qu’ils induisent chez leurs créateurs, ou plutôt leurs promoteurs. Cela autorise non pas une vaine comparaison entre deux modes de traitement de l’information dissemblables, mais un appui stable permettant de répondre à la question autrement plus féconde de leur éventuelle altérité, à laquelle l’article donne réponse.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".