[Debogue tes humanités] IA et la correction textuelle automatique : quels outils et quelles limites ?
Bibliographic record
Abstract
Captation du deuxième atelier de la série "Qu'est-ce qu'IA ?" données à la BLSH par l'équipe de la Chaire de Recherche du Canada sur les Écritures Numériques. Résumé : Les outils d’IA générative se sont désormais immiscés dans tous nos logiciels d’édition, aussi bien pour la rédaction de mail, de documents textuels que pour de l’assistance à la rédaction de fiction ou de dissertation, mais comment faire la différence entre toutes les formes de corrections possibles et mesurer l’intérêt et l’impact de ces outils dans nos pratiques. Cet atelier vise à outiller les chercheur.se.s en SHS sur les outils existants et offrir des pistes pour mesurer leur impact dans leurs pratiques individuelles.
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 | 0.010 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".