A Framework for Modeling Construction Organizational Competencies and Performance
Bibliographic record
Abstract
The variables that characterize construction organizational competencies are both quantitative and qualitative in nature, and thus require measurement methods and modeling techniques that can handle both variable types. Models that are capable of relating organizational competencies to performance provide a critical advantage in the identification of target areas leading to improved performance. This paper proposes a framework to develop a fuzzy hybrid model for mapping organizational competencies to performance. To achieve these objectives, different fuzzy modeling techniques, such as fuzzy rule-based (FRB) systems and fuzzy neural networks (FNNs) are explored. This study highlights research gaps related to organizational competency and performance studies in developing models at the organization level. The proposed framework outlines modeling procedures that enable the integration of fuzzy modeling techniques with other approaches that exhibit learning capabilities. The proposed model captures organizational competencies as input by using various competency evaluation criteria, and provides organizational performance as an output using multiple performance metrics. Finally, the model assists researchers and industry practitioners in evaluating the competencies of construction organizations and in analyzing their impact on organizational performance.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".