Rethinking Public Transit Networks Using Climate Change Mitigation and Social Justice Lenses: Great Victoria Area Case Study
Bibliographic record
Abstract
Public transit has a relatively low GHG-to-passenger ratio and offers affordable options for local travel compared with personal vehicle travel. Investments in an effective public transit network are therefore critical for progress toward climate change mitigation and social justice. Using the Greater Victoria Area (GVA) in British Columbia, Canada, as a case study, this research identified, mapped, and examined three new regional transit network scenarios, which, respectively, align with the objectives in the planning and policy areas of (1) climate change mitigation, (2) social justice, and (3) climate justice. The methods involved a literature review to develop an analytical framework for mapping and examining new transit networks using the climate change mitigation, social justice, and climate justice lenses. The framework was revised through a research practitioner workshop, and it was then applied using network analysis techniques to (re)map the GVA’s transit networks under the three scenarios. The key outcomes of the project included an analytical framework and a process for analyzing and remapping transit networks in ways that align with climate and social justice objectives. The findings indicated the need to add some bus routes and stops, especially in the northern part of the GVA, and two new fast transit networks according to the justice lenses.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".