Implementing a Public Bicycle Share Program: Impact on Perceptions and Support for Public Policies for Active Transportation
Bibliographic record
Abstract
BACKGROUND: Favorable public opinion and support for policies are essential to favor the sustainability of environmental interventions. This study examined public perceptions and support for active living policies associated with implementing a public bicycle share program (PBSP). METHODS: Two cross-sectional population-based telephone surveys were conducted in 2009 and 2010 among 5011 adults in Montréal, Canada. Difference-in-differences analyses tested the impact of the PBSP on negative perceptions of the impact of the PBSP on the image of the city, road safety, ease of traveling, active transportation, health, and resistance to policies. RESULTS: People living closer to docking stations were less likely to have negative perceptions of the effect of the PBSP on the image of the city (OR = 0.5; 95% CI, 0.4-0.8) and to be resistant to policies (OR = 0.8; 95% CI, 0.6-1.0). The likelihood of perceiving negative effects on road safety increased across time (OR = 1.4; 95% CI, 1.2-1.8). Significant interactions were observed for perceptions of ease of traveling (OR = 0.5; 95% CI, 0.4-0.8), active transportation (OR = 0.6; 95% CI, 0.4-1.0), and health (OR = 0.6; 95% CI, 0.4-0.8): likelihood of negative perceptions decreased across time among people exposed. CONCLUSION: Findings indicate that negative perceptions were more likely to abate among those living closer to the PBSP.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".