Devrait-on instaurer un système de péage sur les ponts de l'Île de Montréal - Une étude avantages-coûts
Bibliographic record
Abstract
Je tiens à remercier les personnes suivantes: Monsieur Fernand Martin du département de sciences économiques de l'Université de Montréal pour avoir accepté de me superviser dans ce long travail et pour avoir répondu à toutes mes questions.Catherine Laplante de chez Les Conseillers ADEC pour m'avoir fourni des idées, des données et références qui ont ajouté de la précision à cet ouvrage et sans qui j'aurais sans doute traîné encore plusieurs mois avant de terminer ce rapport.Marie-Line Audy pour avoir gracieusement accepté de corriger mes nombreuses fautes.Karine Daigneault pour ses conversations et ses suggestions intéressantes qui m'ont aidé à commencer ce projet.Jasmine Marceau, pour m'avoir soutenu moralement et financièrement pendant tout le projet.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 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".