Framework for Simulating Crew Motivation Impact on Productivity—A Hybrid Modeling Approach
Why this work is in the frame
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Bibliographic record
Abstract
Previous studies have identified motivation as one of the most important factors affecting the efficiency of labor utilization in construction processes. However, there is a lack of research on simulating the impact of motivation on labor productivity to track and devise productivity improvement strategies. Fuzzy system dynamics (FSD) has been used to model labor productivity, because it captures subjective uncertainties and the dynamism of construction systems. However, FSD fails to capture complexity arising from individual components (e.g., crew members) that lead to emerging behaviors in crew motivation modeling. The main contributions of this paper are: (1) proposing a framework for combining FSD and fuzzy agent-based modeling, leading to a more comprehensive method for studying the impact of crew motivation on productivity; and (2) facilitating identification of more effective productivity improvement strategies by allowing construction stakeholders to track the dynamic relationships between motivation and labor productivity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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