A Novel Gibbs Entropy Model Based upon Cross-Efficiency Measurement for Ranking Decision Making Units
Bibliographic record
Abstract
Cross-efficiency measurement in data envelopment analysis (DEA) was developed to overcome the main disadvantage of DEA in discriminating decision making units (DMUs). However, the results obtained from each cross-efficiency model (Benevolent and aggressive models) may not generally be the same for similar problems, and each model may provide different viewpoints that we should take each model into account at the same time. Since Gibbs entropy is one of powerful tools to measure uncertainty, in this paper a novel linear programming model based on the concepts of Gibbs entropy (GE model) has been offered to combine cross-efficiency scores, which are obtained from the viewpoints of benevolent and aggressive models, for ranking DMUs. In order to validate the proposed GE model, it is tested with two examples, including the performance assessment problem and the relative efficiency of seven Thai provinces. The main advantages of the GE model are that it can be used to tackle large size problems with uncertainty, and it can be used to combine other models for ranking DMUs. In addition, the set of multiple solutions of optimal weights for each model can be ignored. By using the proposed model, decision-makers can achieve more reliable decision than individual models.
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How this classification was reachedexpand
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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".