A Model for Construction Contractor Selection Using Competitive Intelligence (CI)
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
While comprehensive and ongoing competitive intelligence (CI) is employed in a variety of industries to provide valuable input for broad strategic decisions, the construction industry lags behind in adopting this technique. This paper presents a CI model for use in the construction contractor selection process, which is a critical element of construction project management and one that inherently entails risk and risk management. The use of CI for contractor selection is an important development in light of the realization on the part of many companies that the diffuse nature of the information and lack of robust analysis create numerous inconveniences during the decisionmaking process. Based on the application of the CI method for a competitive environment, the proposed model has the potential to improve the process for assessing and selecting contractors. This paper describes the proposed model, including background information, structural details, guidelines for its use and implementation, and key data analysis findings.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it