Evaluating Private Bus Operators’ Willingness to Participate in Transit Improvements in Mexico
Bibliographic record
Abstract
Inputs are provided for the decision-making process of transit improvements in developing countries. With an analysis of the willingness of private bus operators to participate in transit improvements, political feasibility can be assessed, and the likelihood of successful implementation can be increased. Data from 156 surveys conducted in cities in Mexico are used to develop probabilistic models that quantify the influence of private bus operators’ characteristics, perceptions about business and operating efficiencies, and their relationship with government on their willingness to participate. Evidence shows that several elements can increase the willingness of private operators to participate in government-led proposals. These elements include the level of trust and communication between private bus operators and government authorities, the economic power of private bus operators, and the attachment to the status quo. Several features are shown to limit operators’ willingness to participate, including the model of operation, likelihood of lost revenue through taxation, and concerns about the potential modifications of their legal rights to operate. An analysis of Mexico City, Mexico, and surrounding areas demonstrates the need to establish a well-defined strategy for engaging private bus operators in transit improvements; failure to do so has resulted in much less trust of government and led to more conflicts about future projects. The importance of analyzing private bus operators’ participative profiles in the assessment of transit improvements is revealed. Selected areas for improvements might present challenges for engaging private bus operators in proposed improvements.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".