Addressing Perceived Insecurity in Public Transportation: A PLS‐SEM Approach
Bibliographic record
Abstract
The sense of insecurity and fear of crime associated with public transportation are disadvantages that can deter individuals from using public transportation. We propose a methodology based on partial least squares structural equation modeling (PLS‐SEM) to examine perceptions of insecurity among public transportation users in the Gran Valparaíso area and how these perceptions have influenced the corporate image of the services. To do so, we conducted a survey to assess perceptions of insecurity in situations involving harassment, crime, threatening social environments, crowded and isolated environments, and corporate image. Additionally, we considered gender and previous experiences of victimization in public transportation as moderating factors to identify significant differences among these groups. The main factor influencing the corporate image of buses and the metro is insecurity based on harassment. In terms of gender, women have a heightened perception of insecurity in crowded settings, influencing their feelings of insecurity based on harassment. Meanwhile, men’s apprehension mainly relates to crime and is due to isolated environments.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".