Identifying the Leaders
Bibliographic record
Abstract
With increasing recognition of the potential and accrued benefits for mobility, health, and the environment, public bikeshare programs are growing in popularity globally. Any city planning to launch a program will be keenly interested in understanding who may use it to enable strategic marketing that will facilitate quick uptake and adoption. The diffusion of innovation theory was applied to data from a population-based telephone survey to characterize who would be most likely to use a new public bikeshare program. A telephone survey of 901 residents of Vancouver, British Columbia, Canada, was conducted before the launch of Vancouver's public bikeshare program. Results showed that a majority [ n = 614/901, 69.1%; 95% confidence interval (CI) = 66.3%, 72.7%] of respondents thought that a public bikeshare program was a good idea; however, only one-quarter ( n = 217/901, 24.2%; 95% CI = 21.1%, 27.3%) said that they would be likely or very likely to use the program. Logistic regression identified characteristics associated with higher and lower likelihood of use, which were used to create an adoption curve that defines population segments anticipated to be the leaders in adopting the program. The theory was used to develop implementation recommendations to maximize program uptake, including ensuring that the program would have tangible advantages over driving and transit, would be affordable and easy to try, would integrate with transit and carshare opportunities, and would appeal to social trends such as environmental responsibility. These results can assist planning and promotion in cities set to launch public bikeshare 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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.021 |
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".