Benchmarking construction safety performance at a global level: A case study of US, Canada, and New Zealand
Bibliographic record
Abstract
Construction safety plateau has become a global issue. To sustain the continuous improvement of the global construction safety performance, research studies on construction safety performance at a global scale, i.e. comparing safety performance across countries, are needed. To fill in this gap, this paper starts with a preliminary study by comparing the safety performance of the Canada, US, and New Zealand construction sites and by investigating the impact of three demographic factors on construction safety performance of workers, including age, work experience, and union membership. Safety surveys were collected from 2015 to 2017. In total, 837 surveys were collected from Canadian construction sites, 420 surveys were from US construction sites, and 40 were from New Zealand. The major findings are as follows. First, the top five physical injures that were reported most frequently are the same across the 3 countries, including cut, puncture, or open wound, headache or dizziness, strain or sprain, persistent fatigue, and skin rash or burn. Second, the top five unsafe events that were reported most frequently are the same across the 3 countries, including overexerted, slipped, tripped, or fell on the same level, pinch, exposed to chemicals, and struck against something fixed. Third, the most frequently reported unsafe event for all the 3 countries is overexerted. Finally, union membership has an extensive impact on the occurrence of safety incidents for both Canada and US sample. In future, more data are needed from New Zealand construction sites to enable further exploration.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".