Fuzzy trade-offs in data envelopment analysis
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
Production trade-offs represent simultaneous and possible changes to the inputs and outputs in the technology under consideration. However, since trade-offs are illative and subjective, in many real applications, the data of production trade-offs cannot be precisely measured. Occasionally, a crisp trade-off cannot reflect desirable judgment of expert. This paper develops the trade-off approach in data envelopment analysis (DEA) using imprecise trade-offs represented by fuzzy sets. We develop some fuzzy versions of trade-off DEA models by using some ranking methods based on the comparison of α-cuts. We show in numerical examples how our models become useful for detecting sensitive trade-offs. In other words, our approach can be seen as extension of the trade-off approach that provides users with models, which represent real evaluation of decision making units (DMU) with good judgments as possible trade-offs.
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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 | 0.030 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.011 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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