Rational Best-Value Model Based on Expected Performance
Bibliographic record
Abstract
The best-value procurement strategy is gaining the interest of federal and state agencies. The strategy increases the value added to a project for each dollar added. A new concept of best value, that is, a rational and flexible model based on expected performance, is presented. The model's flexibility is obvious in the selection of parameters to be included in the contractor selection process and in the determination of their weights. The model's rationality will be achieved through relating all awarded scores to the agency's expected performance. The establishment of the best-value model relies on the past record of the contractor's work for the agency as an indicator of qualification trend. This research incorporates prequalification as a first-level screening technique in selecting top contractor bids in the best-value procurement and then applies a rational scoring system in the final selection. Selection of the most appropriate contractor with the best qualifications for a given project will be based on contractor best value. Data are collected from groups of experts in the Minnesota Department of Transportation and processed through the analytic hierarchy process to establish the parameter weights. Although this research assists departments of transportation in selecting the best contractor, the results are relevant to both academics and practitioners. The paper provides practitioners with a tool for ranking contractors based on best value and provides academics with selection parameters, a model to evaluate the best value, and a methodology for quantifying the qualitative effect of subjective factors.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".