The role of governance models in the development of transport infrastructure megaprojects in Greater Montreal: The case of the Réseau express métropolitain
Bibliographic record
Abstract
This article focuses on mobility issues in Montreal, whose metropolitan transportation policies are presented as one of the major ambitions of large North American metropolitan areas. Empirically, we are interested in a recent transportation megaproject: the Réseau express métropolitain (REM) in Montreal, an electric light-rail transit network spanning 67 kilometers in the Greater Metropolitan Area. These types of megaprojects involve significant governance challenges and certain criticisms due to the involvement of several actors from different backgrounds and defending different interests, which places. This is why we believe that it is important to address this issue from the point of view of metropolitan governance through the agenda-setting of urban megaprojects. The originality of this article is that it demonstrates how presenting the REM project as a public-public partnership, between the Caisse de dépôt et placement du Québec (CDPQ) and the Government of Québec, opened the door to favoritism for the Caisse which influenced the choice of a political solution in Greater Montreal. By mobilizing Kingdon's model, we conclude that windows of opportunity cannot open without choosing a governance model during the agenda-setting phase.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".