5Q IA CLARTÉ PLUMCr Qc : cinq questions permettant d’appréhender l’usage d’intelligence artificielle pour accroître la clarté du plumitif criminel québécois
Bibliographic record
Abstract
Le plumitif est un registre judiciaire qui permet de garder une trace du deroulement de chacun des dossiers ouverts devant les tribunaux. Plusieurs etudes demontrent que son contenu est difficile a comprendre. Dans le present article, nous presentons une application web qui s’apparente aux logiciels de traduction de texte en ligne et qui permet de transformer le plumitif - un document qui consiste essentiellement en une serie d'abreviations difficiles a comprendre - en un texte clair et suivi. Pour apprehender les enjeux juridiques relatifs a cette preuve de concept, nous nous proposons de repondre aux cinq grandes questions suivantes : qu’est-ce que le plumitif ? Qu’est-ce que la clarte en droit? Pourquoi la clarte du plumitif est-elle importante ? Quels sont les elements qui nuisent a la clarte du plumitif et comment la technologie peut-elle etre un allie pour y remedier ? Quels sont les enjeux a considerer pour l’implantation de nouvelles technologies dans le contexte du plumitif ?
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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; both teacher heads agree on what is shown here.
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".