Evaluating safety performance in manufacturing sector: An enhanced super-efficiency data envelopment analysis approach
Bibliographic record
Abstract
Workplace safety in the manufacturing sector is a critical concern, with high numbers of lost-time injuries and fatalities impacting overall productivity and economic stability. Traditional safety performance evaluation methods rely on lagging or leading indicators, yet these approaches often fail to provide a comprehensive assessment of workplace risks. Data envelopment analysis (DEA) is a valuable tool for systematically evaluating safety performance, considering both lagging and leading indicators. However, the presence of zero values in input data presents a significant challenge for the feasibility of DEA efficiency evaluation models. This study aims to develop a super-efficiency DEA model that effectively addresses these infeasibility issues and manages zero input values. Besides, the model incorporates time-series data to assess safety performance within the manufacturing sector. The proposed model also formulates input savings and output surpluses to ensure a stable and consistent evaluation of workplace safety. This offers a practical tool for policymakers and managers to improve safety performance. The effectiveness of the developed DEA model is demonstrated through its application to safety performance data from the Canadian manufacturing sectors. The findings indicate that Ontario, Alberta, and Quebec achieved the highest levels of workplace safety efficiency in specific years compared to the other provinces. The results also imply that the proposed model provides a more precise evaluation and discriminatory power compared to previous approaches.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".