Industry Stakeholder Perspectives on the Adoption of Electric Bicycles in British Columbia
Bibliographic record
Abstract
Electric-assist bicycles (e-bikes) are an emerging mode of transportation that offers a sustainable alternative to automobile use in urban areas. Past research on e-bike adoption has focused on user perspectives. Understanding other stakeholder perspectives is also essential to implementing effective e-bike policy. The objectives of this research are to identify alignments and misalignments in perspectives on e-bike adoption across industry stakeholders in British Columbia (BC), including e-bike retailers, manufacturers, cycling coalitions, and government agencies, and to provide recommendations for e-bike policy that account for those perspectives. An online survey was distributed to industry stakeholders to examine perceived barriers to adoption, expected impacts of adoption, and effects of policy on adoption. Questions about regulations discriminated between five e-bike types: pedal-assist, throttle-assist, scooter-style, electric recumbents, and enclosed electric recumbents. Results indicate strong agreement among industry stakeholders that scooter-style e-bikes require separate and additional regulation from other types of e-bikes and from existing regulation in BC. In contrast, there was misalignment in the expected mode shift resulting from e-bike adoption, with government agencies least optimistic about diversion of automobile trips. Industry stakeholders broadly agreed on the need for speed regulation and viewed higher speeds as one of the least important benefits of e-bikes, which contrasts with past research on user perspectives. Policy recommendations include reclassifying scooter-style e-bikes, rebate or tax programs to reduce e-bike costs, further research on optimal e-bike speed limits, and continued support for improvements in general cycling infrastructure (a top priority for industry and user stakeholders).
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".