Inequity in the Provision of Public Bus Service for Socially Disadvantaged Groups
Bibliographic record
Abstract
Neo-classical economic doctrine dominating governmental policies shows its impact on recent transport policies, causing these policies; tend to base on demand and efficiency criteria instead of equity concerns. Public transit operating for remote areas is less cost-effective eventually leading to have a low level of service quality. In metropolitan areas of developing countries, a large part of socially disadvantaged and vulnerable groups live in outer suburban locations not in the inner-city. Transit equity evaluation is required by in order to consider the requirements of more vulnerable populations, as well as to foster equal benefits. The evaluation approach is based on highlighting the spatial distribution and clustering patterns of bus network and service as well as some disadvantaged social groups including unemployed, migrated, less educated, elderly, young, and disabled. The hypothesis is that vulnerable groups and economically disadvantaged communities receive a less than equal share of public bus services. The findings show that poor accessibility is associated both with low-income neighborhoods and with neighborhoods with disproportionately high populations of migrated, less-educated, unemployed and low-income groups. Modifications need to make in transport planning and policy system to achieve a better distribution of public transport services in hope of increasing level of service for minority groups and economically disadvantaged communities.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".