Identification and ordering of safety performance indicators using fuzzy TOPSIS: a case study in Indian construction company
Why this work is in the frame
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Bibliographic record
Abstract
Purpose The purpose of this paper is to propose a practical framework to measure the safety performance of workers in the Indian construction industry. The key safety performance indicators are identified and ordered on the premise that the higher order assignment of an indicator implies a strong indication of an effective safety performance. Design/methodology/approach Various indicators of safety performance in the construction industry were identified from extant literature review combined with author's personal viewpoint. The identified variables were inquired for appropriateness for the Indian construction scenario by consultation with experts. Fuzzy Technique for order preference by similarity to ideal solution (TOPSIS) technique was considered for the ranking of the indicators from most to least important. Findings The most important highlight of the study was the importance of the role of management by participating in informing workers about the safety rules and compliance toward safety measures. Proper and timely safety training to the workers and equipping them with sophisticated safety equipment for daily activities is perceived to be highly important in ensuring a safe and healthy workplace environment. Controlling the absenteeism rate reduces the burden of extra work on the employees, thereby, encouraging safe work-related behavior. Originality/value Senior management should make safety induction programs compulsory at the time of joining of the employees. The guidelines for safety practices, rules and information about the safety equipment should be properly documented and arranged in safety manuals. Periodical drills involving visual demonstration of the safety practices should be followed to ensure safety at workplace.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it