A fuzzy method for the selection of customized equipment suppliers in the public sector
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.095 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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