Sociodemographic Factors Associated With Aggressive Driving Behaviors of 3-Wheeler Taxi Drivers in Sri Lanka
Bibliographic record
Abstract
Little is known about the nature and scope of aggressive driving in developing countries. The objective of this study is to specifically examine the sociodemographic factors associated with aggressive driving behavior among 3-wheeler taxi drivers in Sri Lanka. Convenience samples of 3-wheeler taxi drivers from Rathnapura, Ahaliyagoda, Sri Lanka were surveyed from June to August 2006. Analyses included bivariate and multivariate logistic regression. Drivers with less than high school education were 3.5 times more likely to drive aggressively (odds ratio [OR] = 3.46; 95% confidence interval [CI] = 1.08, 11.1). Single drivers were 9 times more likely to run red lights (OR = 8.74; 95% CI = 2.18, 35.0), and being single was a major risk factor for drunk driving (OR = 4.80; 95% CI = 1.23, 18.7). Furthermore, high school completers were 4 times more likely to bribe a policeman (OR = 4.27; 95% CI = 1.23, 14.9) when caught violating the road rules. Aggressive driving and risk-taking behavior are amenable to policy initiatives, and preventive programs targeted at key groups could be used to improve road safety in Sri Lanka. This study demonstrates that aggressive driving behavior is associated with sociodemographic factors, including the level of education, marital status, and other socioeconomic factors. Hence, economic factors should be addressed to find solutions to traffic-related issues. It will be the government's and policy makers' responsibility to try and understand the economic factors behind risky road behavior and bribe-taking behavior prior to legislating or enforcing new laws.
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 | 0.000 | 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 | 0.000 | 0.000 |
| 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".