Dans l'œil de l'Obvia - Comment intégrer l’IA de manière responsable au sein de l’administration publique?
Bibliographic record
Abstract
Le Québec s’est doté d’une Stratégie d’intégration de l’IA dans l’administration publique en 2021. Son objectif consiste à soutenir l’utilisation de l’IA par les organismes publics et à baliser son usage pour améliorer la qualité, l’efficience et l’équité des services offerts aux citoyens. La stratégie est en cours de déploiement. Les attentes sont élevées envers l’IA : réduire la taille de l’État, améliorer la productivité des fonctionnaires, valoriser leur travail (en les délestant de tâches à faible valeur ajoutée) et surtout, améliorer les services à la population. La science a creusé la question de l’intégration de l’IA dans les administrations publiques et peut apporter des enseignements précieux aux acteurs qui auront la responsabilité de mettre en place ces nouvelles technologies au sein de l’appareil de l’État.
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 | 0.033 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.022 | 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".