Better Understanding of Factors Influencing Likelihood of Using Shared Bicycle Systems and Frequency of Use
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
Planning and transportation professionals are promoting a variety of sustainable travel alternatives, such as public transit usage, walking, and cycling, as affordable transportation options to counter the negative effects of widespread car use. In their traditional form, these alternative transport modes do not always offer the flexibility or convenience of the car; therefore, innovative solutions have been developed to allow active and public transport to compete better with the car. Shared bicycle systems have been adopted by a growing number of cities and regions throughout the world, yet little is known about the users of the systems and their motivations. A survey was conducted in Montreal, Quebec, Canada, in the summer of 2010 to determine the factors that encouraged individuals to use the system and the elements that influenced frequency of use. The factor found to have the greatest effect on the likelihood for use of a shared bicycle system was the proximity of home to docking stations. Ownership of a yearly shared bicycle membership was associated with cyclists riding shared bicycles 15 additional times per year. Respondents indicated that they valued the shared bicycle's trendy status and the role that it could play in bicycle theft prevention. The potential of shared bicycle systems can be maximized by increasing the number of docking stations in residential neighborhoods and by emphasizing the popularity of shared bicycles and theft prevention in advertising campaigns.
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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