The Health Effects of Fixed-Guideway Transit Investment : A Review of Methods and Best Practice [executive summary]
Bibliographic record
Abstract
[This executive summary was prepared for: City of Vancouver Vancouver Coastal Health Authority TransLink] A growing body of evidence suggests that transportation and land use investments can have broadreaching implications for population health, economic development, and greenhouse gas emissions. Transportation systems link people with social and health promoting resources, such as employment, education, food, recreation, social services, and health care. When developed thoughtfully, transit investment can be lead to more compact urban development conducive to active transportation. Transit investment thus can influence healthy behaviors such as walking and biking. Fixed alignment transit such as rail rapid transit, light rail transit and bus rapid transit, are important regional investments that influence land values and are linked with economic development, housing, and social justice strategies. As the City of Vancouver, Vancouver Coastal Health Authority, Metro Vancouver, and TransLink prepare for the Millennium Line Broadway Extension (MLBE) rail rapid transit investment; this report provides a review of methodologies of pre/post evaluations, strengths, weaknesses, and applicability to the MLBE. It also documents the edge of current academic knowledge that may be augmented through thoughtful studies implemented around the MLBE.
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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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".