Vers un modèle de maturité pour la gestion documentaire augmentée dans les organisations contemporaines: composantes et stratégie de mise en oeuvre
Bibliographic record
Abstract
Au cours des dernières années, on observe un intérêt croissant envers l’automatisation des pratiques de gestion documentaire, grâce à la mise à profit des outils d’intelligence artificielle (IA), en parlant notamment d’une gestion documentaire augmentée. Cependant, l’intégration effective et efficace de l’IA aux processus archivistiques commande l’existence d’un ensemble de requis informationnels, technologiques, managériaux et normatifs. Cela ferait en sorte que les organisations puissent s’emparer du potentiel de l’automatisation pour soutenir leur devoir de transparence et améliorer leur performance. Le but de cet article est de proposer un modèle de maturité de six niveaux, mettant de l’avant les requis du déploiement de la gestion documentaire augmentée, de même que les pratiques et les dispositifs nécessaires à leur opérationnalisation. Il est aussi question d’identifier les aspects organisationnels à prendre en considération pour favoriser la transition d’un niveau de maturité à un autre. Mots-clés : maturité; intelligence artificielle; gestion documentaire; automatisation; gouvernance d’information.
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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 | 0.003 | 0.007 |
| 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 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".