Differentiation of Evaluation Criteria in Design-Build and Construction Manager at Risk Procurements
Bibliographic record
Abstract
The procurement processes used in the alternative contracting methods of design-build (DB) and construction manager at risk (CMAR) are heavily focused on best-value and qualifications-based selection. However, previous research has not examined the effectiveness of owners' evaluation criteria in differentiating among competing bidders. The objective of this study was to document the selection outcomes of the bidders in DB and CMAR projects and identify which evaluation criteria had the greatest differentiation in scores for competing bidders. The results were compared with previous research on the procurement of architectural and engineering consultants and design-bid-build (DBB) contractors. The study sample consisted of 362 bidders for 63 DB and CMAR projects in the United States and Canada. The statistical analysis results showed that scores on interviews and technical proposals had the greatest differentiation, while cost proposal scores had minimal differentiation. These findings provide practical guidance for owners and bidders regarding how to prioritize evaluation criteria and how to respond to them.
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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 | 0.124 | 0.217 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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 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".