Perceptions and Strategies: An Analysis of Gendered Safety Perceptions and Mitigation Strategies for Public Transportation
Bibliographic record
Abstract
This research paper investigates how different safety measures influence safety perceptions across genders and shape overall security experiences within public transportation. The study utilizes primary data analysis from a survey focusing on perceptions of safety among users of the Edmonton Transit System, highlighting significant differences between gender and feelings of unsafety as well as preferences for increased safety strategies. The findings suggest that there is a disparity in safety perceptions between genders, with female respondents feeling more unsafe compared to male respondents. Additionally, a content analysis of multiple safety-related documents was conducted to deduce safety perceptions and mitigation strategies. The paper emphasizes the importance of considering gender-specific needs in the design of public transport systems to create a more inclusive and secure environment for all passengers. Overall, this research contributes to the understanding of the intricate relationship between transit safety measures and gender-specific safety perceptions, providing insights for the development of more effective safety strategies in public transportation systems.
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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 | 0.002 | 0.006 |
| 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".