Enhancing public transport use: The influence of soft pull interventions
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
Public transport (PT) success depends on targeted interventions, ranging first from push measures that discourage car use to pull measures that encourage PT use, and second from hard measures that intervene at physical infrastructures to soft measures that intervene at psychological elements of individuals’ behaviors. Focusing on soft-pull policy measures, and through a scoping review of 36 publications, we categorize these measures into three overarching groups: 1) Internally motivating strategies that gradually but firmly instill pro-sustainability attitudes and norms in people’s mind; 2) Satisfaction increasing strategies that primarily help retain current users especially those who feel forced to use PT and secondary attract new riders by improving the service factors and modifying travelers’ inaccurate perceptions of the service; 3) Stimulating PT-use and car-habit disrupting strategies such as attractive incentives and tailored information that encourage auto-drivers to give PT a try and break their car-habit. This review provides an analytical evaluation of each approach, offering recommendations for policy makers and PT service providers, along with identifying research gaps and suggesting future research directions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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