A Framework for Modeling Construction Organizational Competencies and Performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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