Criminality and crime control measures in selected train stations in Lagos, Nigeria
Bibliographic record
Abstract
Crime is among the major problems negatively impacting the effective operation of the rail transportation system in Nigeria. Although considerable scholarly attention has been devoted to criminality in public transit stations, there is a paucity of empirical data on the occurrence of the problem within train station facilities. Thus, using routine activity theory as a guide, this study investigated criminality and crime control measures in selected train stations in Lagos, Nigeria. In-depth interview and key informant interview methods were primarily deployed to gather data from 20 train station officials and eight locomotive drivers selected using purposive sampling technique. Results showed that vandalism, pilfering of train station equipment, rooftop riding and ticket evasion were the most commonly recorded forms of crime in train stations in Lagos. Multiple situational and environmental factors, including the presence of abandoned equipment, lax security systems, the construction of train stations in residential neighbourhoods, and poorly illuminated environments were making train stations vulnerable to criminality. It is imperative for the Nigeria Railway Corporation to strengthen existing security architecture at train stations to effectively deter motivated offenders from viewing the public transportation hub as suitable sites for crime perpetration.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".