Count Models Analysis of Factors Associated with Road Accidents in Nigeria
Bibliographic record
Abstract
The current state of all Nigerian roads is in poor condition, and reports of accidents have been recorded across the federation. The larger mission of the sustainable development goal is to promote sustainable cities and communities. This research study aims to examine factors responsible for road accidents in Nigeria through the quantitative tool of higher extensions of the Poisson regression model (ZTNPRM). A cross-sectional study design was adopted and secondary data was used within a sample period from the 1st quarter of 2006 to the 2nd quarter of 2020. Due to overdispersion, ZTNPRM indicates human errors contribute to a large proportion (41.4%) of road accidents. Vehicle factors are also statistically and positively related to road accidents. All the factors this model identified that lead to road accidents predicted low road accidents. Hence, the study recommends that Nigerian car users follow all rules and regulations associated with safe driving and make the environment safer for people as the sustainable development goal (SDGs). This study recommends more attention to the area of accident and injury prevention as a strategic objective of the SDGs.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".