A fuzzy data envelopment analysis based on credibility theory for estimating road safety
Bibliographic record
Abstract
Road accidents as a global challenge, imposing irreparable financial and human life losses in almost all countries, especially in developing countries, annually.According to world health organization (WHO), if this trend continues, road accidents will become the 7th cause of human death by 2030.Thus, road safety policy makers have been trying to use safety promotion and preventative actions.In this paper, the road safety performance of Iranian provinces is studied.To evaluate road safety efficiency scores, data envelopment analysis based on road safety (DEA-RS) method in two deterministic and non-deterministic situations is used.To consider the uncertainty in input and output data, this paper develops credibility DEA-RS (CreDEA-RS) model.In fact, the constraints of DEA-RS model are considered as credibility constraints and a counterpart credibility DEA-RS (CreDEA-RS) model is proposed for evaluating road safety of provinces of Iran.According to the results, provinces located in mountainous and forest areas such as Gilan had a much weaker performance than provinces in desert areas such as Yazd..
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".