[Revue 3.0] Atelier « Recherche et IA »: Séance du 27 février 2025
Bibliographic record
Abstract
Troisième séance de l'atelier « Recherche et IA » dans le cadre du projet Revue 3.0 de la Chaire de recherche du Canada sur les écritures numériques (27 février 2025, en ligne). Présentations de: - Gérald Kembellec, professeur au Conservatoire national des arts et métiers de Paris (Cnam) - Joaquine Barbet, doctorante au Cnam Dans cette séance: l’explicabilité des Grands modèles de langage (LLMs), les possibilités d’hybridation entre ces modèles et les approches déductives s’appuyant sur un cadre de connaissances explicites. Exploration d’un point de vue théorique (avec quelques cas d’études pratiques) les relations possibles entre les approches connectivistes et symboliques de la connaissance dans le contexte de la recherche.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.023 | 0.011 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.162 | 0.126 |
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".