Crew Cost and Productivity Performance Benchmarking Based on Commercial Cost Estimating Databases
Why this work is in the frame
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Bibliographic record
Abstract
Benchmarks of crew cost and productivity performance for commonly used methods in the construction industry are readily accessible through industry-wide commercial estimating databases (such as RS Means). The objective of this study is to identify limitations of commercial benchmarking services and further propose a framework for benchmarking crew cost and productivity performance over several years and in different cities. The proposed framework is applied to a typical open-cut method, illustrated with data retrieved from RS Means online databases. In particular, the data structure of RS Means is explained, and the drawback of the underlying productivity benchmarking methodology is revealed. We conclude crew cost data ($/labor-hour) from RS Means can be used to trend the changes of time and location-specific crew and material costs in a reliable way, while productivity (daily or hourly production: unit/h) accounts for the industry average only, ignoring the unique variations and attributes of crews, jobs, contractors, or projects.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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