Pedal Power: Operational Models, Opportunities, and Obstacles of Bike Lending in North America
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Bike lending offers a service that enables individuals to borrow bicycles for short-term use (i.e., ranging from 2 hours to 36 months), typically from designated locations within cities, campuses, or communities. Unlike bikesharing systems that typically rely on automated kiosks and/or undocked and free-floating devices for public access, bike lending involves a managed program with staff, similar to a library model. These programs can be administered by community organizations, bike shops, public libraries, and other local entities. They are typically community- or membership-based, with many programs associated with non-profit organizations or publicly owned and operated. In this paper, we investigate bike lending in the United States and Canada as of Spring 2024, including a literature review, the identification and characterization of bike lending programs (n = 55), expert interviews (n = 24), a survey of bike lending operators (n = 31), and 2 focus groups with a total of 12 participants. Insights from expert interviews and operator surveys highlight the experiences of professionals involved in bike lending. The focus groups capture the experiences of bike lending users. This paper finds that North American bike lending is often tailored to the specific needs of communities, such as youth, low-income individuals, and the general population. More sustained funding could support program expansion and diversify bike offerings. Enhancing cycling infrastructure, such as adding dedicated bike lanes and paths, could improve overall cycling safety and increase participation in bike lending programs. This study’s findings could help strengthen existing bike lending programs, guide the development of new initiatives and supportive policies, and enhance safe bicycle use for participants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it