L’intelligence artificielle et la gestion documentaire : quels apports? Quels enjeux?
Bibliographic record
Abstract
Dans un contexte organisationnel marqué par le déploiement massif des plateformes du télétravail, les pratiques de gestion documentaire deviennent de plus en plus hétérogènes. L’absence d’une véritable harmonisation de telles pratiques engendre des défis au niveau du repérage de l’information documentaire, que ce soit à des fins de réalisation des processus d’affaires, de transparence ou encore de reddition des comptes. Une piste prometteuse pour pallier ces enjeux est de mettre à profit les fonctionnalités de l’intelligence artificielle à des fins de gestion de l’information organique et consignée. Cet article se propose d’aborder la manière dont l’intelligence artificielle pourrait optimiser la gestion documentaire, en mettant de l’avant les mécanismes de gouvernance à déployer à cette fin.
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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".