Public policy tools to implement transit-oriented development: The case of the Montreal city-region
Bibliographic record
Abstract
Over the last 25 years, Transit-oriented development (TOD) has become one of the core models of planning in NorthAmerican cities. However, the implementation of TOD is still hindered by various barriers (institutional, political, economical, etc.) leading cities to develop specific policy tools to support TOD. But while TOD has generated a substantialbody of research, literature has paid little attention to its implementation processes, from metropolitan-scale planning to local urban development projects, and the policy tools designed by governments to support TOD. To fill this gap, this paper shifts the focus by analysing the public policy tools used to support TOD implementation, based on a case study of Montreal. It argues that the inclusion of TOD in metropolitan-scale planning has led metropolitan government to develop new public action tools to support the implementation of TOD. It proposes a typology of these tools (regulatory, informative, institutional, good practices and project-based) and examines their differentiated uses and implementation. It demonstrates that these instruments—mostly incentives—are both mirrors and vectors of changes in the planning and governance of the Montreal city-region, favoring collective learning about TOD, new inter-institutional cooperation and shifts in planning practices. This work thus provides empirical evidence of the renewal of planning strategies and the diversification of public policy tools associated with transi
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".