Integrated Urban Mobility for Our Health and the Climate: Recommended Approaches from an Interdisciplinary Consortium
Bibliographic record
Abstract
Background: The purpose of this paper is to suggest an approach to aid with the creation of an interdisciplinary team and evidence-informed solutions addressing the urban mobility challenges facing many communities. Methods: We created a local Urban Mobility Consortium with experts from different disciplines to discuss the development of healthy, accessible communities, electrification, ride-sharing, and overarching issues related to urban mobility. A workshop and survey data collected during COVID-19 are presented in this paper. Results: Several evidence-informed recommendations are provided. Broadly, these were: (1) support the creation and development of accessible and safe active-transportation infrastructure; (2) incentivize and prioritize the use of active, public, and shared transportation over use of personal vehicles; (3) ensure connectivity of active transportation infrastructure with major destinations and public transportation options; (4) work towards electrification of personal and public transportation; and (5) work across siloes to improve integrated mobility to impact climate and health related outcomes, and enhance overall efficiency. Conclusions: An integrated approach is needed to improve mobility, access, and environmental impact. This needs to be carried out in the local context and requires government and non-governmental leadership.
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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 | 0.121 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.010 | 0.050 |
| Research integrity | 0.020 | 0.016 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".