An Inclusive Public Transport System for Riders Wearing Religious Attire
Bibliographic record
Abstract
Ensuring an inclusive and equitable public transport system remains a major challenge for many cities. Spaces in crowded terminals and vehicles can compel riders to interact close to each other. Some evidence shows that public transport users from racial- or ethnic-minority groups, especially individuals who wear religious attire, often encounter higher rates of discrimination and harassment. The present study contributes to the limited knowledge on their travel experiences by providing insights into perceived personal security and ridership frequency, specifically focusing on riders who wear religious attire. This study uses data involving 524 participants from an online survey administered in Auckland, New Zealand. Results showed that racial and ethnic groups are dependent on public transport in their daily lives, and those who wear religious attire are often frequent riders. Findings show statistically significant differences in perceived personal security between those who wear religious attire and others. Individuals with a religious appearance express greater concern about their personal security while riding public transport and are found to avoid using it during off-peak hours. These findings provide evidence that such groups are marginalized because of their appearance. Othering behaviors do not go unnoticed and can create a hostile environment for public transport users who wear religious attire. Findings from this study add to the growing evidence that an inclusive environment is necessary for all riders to feel safe, particularly those who are dependent on public transport. Service providers have an ethical responsibility to ensure a no-tolerance culture for discrimination toward riders that explicitly protects those of marginalized identities, including those who wear religious attire.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".