An improved cross-ranking method in data envelopment analysis
Bibliographic record
Abstract
Cross-efficiency evaluation is an extension of data envelopment analysis methodology for fully ranking decision-making units (DMUs). Based on the idea that ranking order is much more meaningful than the individual efficiency score in several circumstances, a cross-ranking matrix was recently introduced. The matrix was built by replacing the efficiency score in the conventional cross-efficiency matrix with the ranking order of that efficiency score in each row. However, the non-uniqueness issue in the cross-efficiency scores in the cross-efficiency evaluation may result in different ranking orders in the matrix and may thus limit the usefulness of cross-ranking method. This study improves the cross-ranking method by introducing an interval cross-efficiency matrix in cross-efficiency evaluation. The interval cross-efficiency matrix can reveal all possible cross-efficiency scores through self-evaluation and peer evaluation. Therefore, the derived cross-ranking matrix based on the interval cross-efficiency matrix can show all information on possible ranking orders for all DMUs. The improved method is illustrated by two numerical examples.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".