Perspectives of Contractors and Insurance Companies on Construction Safety Practices: Case of a Middle Eastern Developing Country
Bibliographic record
Abstract
The construction industry has long been a major contributor to worldwide occupational injuries and fatalities. The construction industry in Lebanon, a developing country, is no exception in contributing thousands of occupational injuries annually. Previous studies concluded that most Lebanese contractors do neither adopt proper safety practices nor properly implement safety manuals, especially with the absence of governmental enforcement and safety control. Moreover, insurance companies aggravate the existing problem through adopting shaky methods of evaluating premiums, which solely considers the contractor’s unreliable history of accidents. As such, a contractor safety index is proposed, which aims to assess a contractor’s safety status by evaluating the safety practices that the contractor implements. This index can be used by insurance companies when evaluating premiums can motivate contractors to enhance their safety practices in order to achieve a lower premium rate. The current paper presents and analyzes the results of a survey conducted with contractors and insurance companies to evaluate common construction safety practices that will be adopted within the proposed index. Results can help identify which practices would be more impactful on work progress and insurance premiums according to the perspectives of contractors and insurance companies respectively. Findings of the paper aim to improve the existing safety standards and promote a safety culture in the construction industry in Lebanon and other developing countries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".