La gouvernance du métro de Montréal : le financement des activités et des infrastructures
Bibliographic record
Abstract
Plusieurs perspectives d'analyse pour ameliorer l'efficacite des systemes de transport en commun s'offrent au chercheur. Les dimensions techniques font l'objet d'etudes nombreuses, comme le type de transport en commun utilise (tramway, metro, autobus, etc.) ou l'integration des technologies de pointe pour le controle du trafic. L'amenagement des villes et la conception des reseaux routiers et de transport constituent egalement des elements centraux. Nous allons, de notre cote, poser l'hypothese que les sciences sociales, et notamment le droit, offrent une perspective importante souvent oubliee. Effectivement, les modes d'organisation, de controle et d'exercice des pouvoirs et des competences confies aux organismes de transport en commun jouent un role tout aussi determinant pour ameliorer la place des transports en commun au sein d'une communaute. Nous ciblons notre analyse sur les modes d'organisation, de controle et d'exercice des pouvoirs d'un des grands systemes complexes de transport en commun de la metropole du Quebec : le metro de Montreal, qui, par le volume colossal de deplacements d'individus dont il est responsable, constitue l'epine dorsale des transports de masse dans la region metropolitaine. C'est donc la legalite du phenomene qui nous interesse, ou les preoccupations resident dans la formalite de la gouvernance, par opposition a l'aspect informel des relations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".