Road Traffic Accidents in Saudi Arabia: An ADRL Approach and Multivariate Granger Causality
Bibliographic record
Abstract
The present paper examine the nexus between road traffic accident (RTA) and some relevant variables in Saudi Arabia over the period 1971- 2012, using the autoregressive distributed lag ARDL model (Pesaran and Shin, 1999) for co-integration in Saudi Arabia, with the co-integration test. Results show that the variables are co-integrated in Saudi Arabia, moreover, the overall Granger causality results present that road traffic accidents, population and GDP, road mails, registered vehicles, and the number of driver license are Granger-causes each other in Saudi Arabia. With these findings, we affirm that there is a strong relationship and effect between road traffic accidents and its population, GDP, road mails, registered vehicles, and the number of driver license. The findings suggest that the coefficients are negative signed and statistically significant in all VECMs, implying that there is bi-directional causality between the variables of interest in the long run.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".