Municipal Perspectives and Best Practices for Equitable Shared E-Scooter Programs
Bibliographic record
Abstract
Shared dockless electric scooter (e-scooter) programs are rapidly expanding in municipalities throughout U.S. and Canadian cities. Programs are implemented with regulations set out in policy documents. As municipalities adopt programs with transportation goals in mind, there are potential equity implications to tackle transportation inequities sustainably. This study integrates the perspectives of 22 municipal practitioners working on shared e-scooter programs with best practices set out in 5 policy documents with effective regulations. Key themes between practice and policy and lessons for the equitable municipal implementation of shared e-scooter programs are determined. The results show that through good policy that considers lessons learned by municipal staff managing relations with operators, shared e-scooter programs can offer an equitable and sustainable mobility choice. Recommendations are made to intrigue and assist municipal shared e-scooter practitioners in creating equitable and sustainable shared e-scooter programs.
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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 | 0.037 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.006 | 0.006 |
| 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".