Gouvernance municipale et information: étude exploratoire sur l’utilisation optimale des données 311
Bibliographic record
Abstract
Sommaire L’article présente une recherche exploratoire portant sur le potentiel d’utilisation de l’intelligence artificielle pour analyser les informations obtenues à travers les requêtes effectuées auprès de centres municipaux d’appels non urgents (CANU). La recherche établit trois utilisations possibles des informations provenant d’un CANU soit pour des fins de gestion interne, de prestation de services ou de prise de décisions stratégique. Ces utilisations de l’information sont explorées à travers l’analyse du CANU de la Ville de Gatineau. Les résultats établissent deux séries d’hypothèses pour favoriser une utilisation optimale des données amassées à l’aide d’outils numériques sophistiqués. La recherche montre que les CANU pourraient être des nœuds informationnels consacrés au traitement, à la répartition et à l’analyse stratégique des informations.
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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 | 0.020 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".