A feedback-guided analysis of environmental, health and socio-economic factors affecting drivers’ willingness to shift to e-jeepneys
Bibliographic record
Abstract
• Feedback-guided analysis surfaces assumptions and decision-making paradigms. • Economic well-being greatly influences shift compared to health and environment. • Drivers prioritize take-home pay over other benefits in deciding to shift to e-jeepneys. • Low willingness to pay for intangible benefits of jeepney modernization among drivers. The Jeepney Modernization Program involves the replacement of traditional jeepneys with more efficient alternatives, including modern enclosed electric vehicles, alongside operational improvements to the public transportation system. This study aims to assess the environmental, health and socio-economic factors impacting drivers’ willingness to shift to e-jeepneys. Feedback-Guided Analysis was used as a framework for designing an interdisciplinary approach towards understanding the decision-making paradigms of drivers plying a specific route in Quezon City, Metro Manila. Measurements of drivers’ exposure to PM 2.5 and associated health parameters were complemented by key informant interviews delving into income, expenses, valuation of benefits and decision-making paradigms. A stock-and-flow model integrated the data to quantify costs and benefits of conventional vs. electric jeepney drivers. Results show that conventional jeepney drivers are unlikely to shift to electric vehicles if the projected take-home pay does not support daily expenses, despite the additional benefits of the modernized system including lower exposure to PM 2.5 . Of the daily wage scenarios tested, only the highest at Php 900/day met the threshold for drivers’ needs. The socio-economic feedback in terms of take-home pay dominated over the health and environmental feedback on worldviews. Understanding these stakeholder contexts and priorities is a crucial step towards building trust, co-developing interventions, and overcoming barriers to policy implementation.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".