Analysis of Human and Cultural Factors Causing Risk of Accidents in Jordanian Drivers
Bibliographic record
Abstract
The major objective of this research paper is to investigate the Traffic Safety culture among Jordanian drivers, and to identify common aggressive behaviors, and its association to diverse social factors. An online survey questionnaire was distributed among drivers in Jordan; samples were collected in the Amman area in 2019. The data analysis of the survey was analyzed using the statistical program SPSS. The survey questions were based on methods found in the (AAA foundation’s Annual Traffic Safety Culture). It was found that drivers in Jordan have high exposure to traffic accidents, where one in eight drivers has been injured in a traffic accident, and nearly one in three Jordanians knows someone killed in an accident. 75% of participants consider aggressive driving as a very serious threat, 60% have honked the vehicles horn excessively within 30 days, 36% often failed to signal when turning or stopping. The most common behavior for drivers during the last 30 days according to the survey is “speeding through yellow light 85%, followed by lack of Seat belt use 80%, and followed by Mobile phone use while driving78%, and followed by Speeding on highways 61%. Male drivers were more aggressive, drivers were more exposed to car accidents, they were 1.3 times to get involved in a vehicle damage accident, and four times as female to get involved in a severe injury accident. In addition, significant differences in behavior were found among drivers when compared under surveillance of police and traffic cameras, and when there is no surveillance. 88% of drivers would never cross the red light, 60% would never use the mobile phone, and 75% would never speed if they knew there was a camera or if police were around. Preventive countermeasures were recommended to increase the safety culture awareness of drivers in Jordan.
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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 | 0.001 | 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".