A fuzzy method for the selection of customized equipment suppliers in the public sector
Bibliographic record
Abstract
The acquisition of customized equipment usually requires the selection of a technology supplier to accomplish a developmentproject. This requires the evaluation of the suppliers’ proposals that may be assessed by different evaluators in different ways(single numerical values, intervals or linguistic values). In the public sector, this process may require the prior publication ofthe scoring rules in a request for proposal (RFP). This may force the evaluators to assign weights in advance to characteristicswhose technical significance is known but whose significance for the evaluation is unknown. An inappropriate assignation ofweights in the evaluation may lead to wrong conclusions. The objectives of the research were the implementation of a methodfor the evaluation of offers, including the adaption of weights as part of the evaluation process without violating the principles oftransparency and non-discrimination that are generally required by the legislation; the integration of quantitative and qualitativecriteria in a flexible procedure; and the verification for possible rank reversals. This paper proposes the use of trapezoidal fuzzynumbers (TFN) for the simultaneous implementation of different types of evaluations, incorporates variable weights analysis(VWA) for the subsequent adjustment of weights, and proposes a simple method for the detection of rank reversal. A numericalexample is presented using data from an actual case.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".