Data-driven Approaches to Discovering Knowledge Gaps Related to Factors Affecting Construction Labor Productivity
Bibliographic record
Abstract
Construction labor productivity remains of great importance because of its direct effect on project costs. Numerous parameters (factors and practices) that critically affect labor productivity have been identified in past studies through expert knowledge obtained from surveys. The objective of this paper was to explore whether there is a gap in experts' knowledge in identifying the critical parameters by comparing their perspectives to the results of data-driven analyses of the parameters and labor productivity field data. This paper presents a methodology for identifying critical parameters using both a factor survey and a data-driven approach. The factor survey approach ranks the critical parameters based on the responses of both project management and trade level personnel on a project. The data-driven approach ranks the parameters based on their degree of influence on productivity through filter feature selection on data collected from the actual project. Results of the comparison of factor rankings from the project management perspective, trade perspective, and data-driven approach indicate a major discrepancy between the experts' perspectives and the data-driven results suggesting a need for verification of expert-based results with additional field studies of factors affecting labor productivity.
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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 | 0.062 | 0.196 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.026 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".