Evaluating Transportation Policies and Practices in Canada’s Largest Municipalities
Bibliographic record
Abstract
Land use planning and transportation planning are linked and influence each other in complex ways, but they continue to be treated as separate in practice. Successful integration of land use and transportation can lead to decreased traffic congestion, improved public transit, and reduced greenhouse gas (GHG) emissions, while weak connections can result in sprawling patterns of land development, increased automobile dependence, and poor air quality. The purpose of this project is to investigate leading practices used to integrate land use and transportation planning in Canada’s largest municipalities. This is accomplished through a systematic review of the land use and transportation planning scholarship, and content analysis of municipal official plans from thirty of the largest English-speaking municipalities in Canada based on a plan quality evaluation framework. Three key findings are presented: 1) social justice and equity and economic sustainability were rarely discussed in relation to transportation and land use planning, despite being prominent in the planning literature, 2) there was an absence of rigorous data to inform the fact base of official plans, as well as a lack of data for monitoring and evaluating transportation goals and policies, and 3) while most municipal official plans included a broad section dedicated to implementation, few provided detail on how, when, and by whom transportation-related policies would be implemented. The implications for land use and transportation planning are also discussed.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.026 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".