Decision to bid or not to bid: a data envelopment analysis approach
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
One of the most crucial decisions that is regularly exercised by construction contractors is to determine whether to bid or not to bid on a certain project. The purpose of this paper is to propose a data envelopment analysis (DEA) approach for the bid–no-bid decision. DEA is a robust non-parametric linear programming approach that is used for benchmarking performance and for making selection decisions. Based on a contractor's database of previous considerations of bidding opportunities, DEA creates a “favorable frontier” that consists of favorable bidding opportunities. New bidding opportunities are evaluated in reference to this “favorable frontier” and the bid–no-bid decision is consequently made. The proposed approach incorporates subjective management expertise and deals systematically with bidding situations to guide contractors in their bid–no-bid determination. A major strength of the proposed DEA approach is that it is deployable by organizations facing the bid–no-bid problem regardless of size, country of operation, number and type of factors considered in bidding, or even industry.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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