Identifying Multilevel Metrics for Construction Competency and Performance Measures
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
Construction competencies are combinations of skills, knowledge, technologies, other resources, and practices of a construction organization that contribute to increased effectiveness, competitiveness, profitability, and performance. Previous studies have developed mechanisms to identify and develop construction competencies that aid in performance measurement at project and organization levels, separately. In reality, construction organizations are project-based organizations with complex interactions between competencies influencing performance at different levels. The challenges associated with multilevel construction competency measures include identifying the interrelationship between competencies at different levels and relating multilevel competencies to multilevel performance measures. To address these challenges, this paper provides a review of the literature related to multilevel construction competency frameworks and performance measurement methods. Based on an analysis of the literature, a multilevel framework is developed and presented for construction competency and performance measures. Finally, a data collection approach is provided that will assist researchers and industry practitioners in evaluating construction competencies and performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 it