Development of Calculation Method for Full-Time Equivalent Workers per Man-Year to Improve Fatality Rate Estimation
Bibliographic record
Abstract
The fatality rate in the construction industry is a key indicator for assessing safety management, with the number of workers being a critical factor in its estimation. Many countries rely on sampling inspections or assumptions to determine the number of workers, which can lead to inaccuracies in evaluating the fatality rate. In this study, we developed a method to calculate the full-time equivalent workers per man-year (FTEWm·y) to more accurately estimate the fatality rate, taking into account building and work types using daily work reports (DWRs). The research process included six steps: (i) selecting a target project; (ii) establishing a database; (iii) developing the FTEWm·y framework based on the DWR; (iv) validating the framework; (v) calculating the FTEWm·y for residential building projects in the Republic of Korea; and (vi) applying the framework. The key findings included the following: the FTEWm·y/USD for residential projects was 1.1 × 10−3 FTEWm·y/USD, with the framework achieving an accuracy of 85.30% and an R2 value of 92.92% through five-fold cross-validation. The FTEWm·y for residential buildings in the Republic of Korea was 4.5 × 107 FTEWm·y, and the fatality rate was 0.011‱. This framework offers a more precise way of evaluating fatality rates by considering specific building and work types, improving safety management practices in the construction industry.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".