Analysis of Events and Exposures Leading to Construction Injuries in Developing Countries: The Case of Lebanon
Bibliographic record
Abstract
Abstract There is unanimous agreement that the complex and dynamic nature of construction jobsites is coupled with inherent risks. Global occupational and health agencies revealed that the construction industry leads with the number of fatal injuries incurred every year. Lebanon, a developing country in the Middle East, is no exception. Studies on construction safety in the country showed that the industry has failed to implement safety regulations, while also witnessing a lack of safety incentives, training, and education. The situation worsens with the absence of a national experience multiplier that could reflect on contractors’ safety statuses and hold them accountable for their safety performance. This study addresses the problem by proposing a cost model that evaluates construction injuries. An analysis was performed on more than 3,000 accident claims collected from insurance companies to understand the events and exposures that construction labour face. Results showed that there are 17 different types of events and exposures which can be grouped into three clusters depending on their frequency and average cost. This paper presents and expands on the three clusters and identifies the events that fall under each of them. The findings will serve as the base of the cost model in a future study.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".