Navigating Safety: Security Concerns in College Student Transportation in Algiers
Bibliographic record
Abstract
This study investigates the security concerns of college students commuting in Algiers, focusing on how these interconnected issues impact their safety satisfaction.Utilizing a mixed-methods approach, the research combines quantitative data from a survey of 434 students with qualitative insights from in-depth interviews with 120 students.The findings reveal significant dissatisfaction with the safety of public transportation, particularly buses, trains, and tramways, due to interconnected issues like overcrowding, harassment, and theft, rooted in systemic failures such as inadequate monitoring and poor infrastructure.Comfort satisfaction emerged as a key factor influencing overall safety perceptions, with COUS (student buses) associated with higher safety satisfaction compared to other modes.Demographic factors like gender and age had minimal impact on safety satisfaction, though qualitative data highlighted the pervasive nature of harassment, especially for female students.The study recommends enhancing surveillance systems, increasing the frequency of transport services, and implementing gender-specific safety measures.While certain recommendations, like mobile apps and awareness campaigns, are immediately feasible, more systemic changes, such as infrastructure upgrades, may face financial and bureaucratic challenges.This research contributes to the literature on urban transportation safety and provides practical recommendations for improving the commuting experience for college students in Algiers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".