Integrated FAHP-FDEMATEL for determining causal relationships in construction crew productivity modelling
Bibliographic record
Abstract
Construction crew productivity is affected by the motivation of the crew performing given activities and by situational/contextual factors forming the dynamic construction environment. The literature lacks a comprehensive analysis of causal relationships between crew motivation and situational/contextual factors for dynamic modelling of crew productivity. The contributions of this paper are (1) identifying a set of criteria for performing expert weight assignment for heterogenous group experts in productivity research, (2) proposing an integrated fuzzy analytic hierarchy process–fuzzy decision-making trial and evaluation laboratory approach that provides a systematic, structured method for determining causal relationship mapping between factors affecting crew productivity, and (3) proposing an approach for identifying cause-and-effect groups among the situational/contextual factors and crew motivation, which can further be used to formulate strategic productivity improvement solutions. The proposed methodology is demonstrated using a case study on an actual industrial construction project in Alberta, Canada.
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How this classification was reachedexpand
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".