Stability analysis and enhancement of super-efficiency model based on space distance
Why this work is in the frame
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Bibliographic record
Abstract
The traditional super-efficiency data Enveloping analysis (DEA) model can further distinguish the efficiency of efficient DMU. However, this distinction is unstable when there are perturbations in the efficient DMU inputs and outputs. The spatial distance can reflect the spatial variation of DMU on the envelope surface. We investigate the stability of the modified VRS super-efficiency model in the presence of data perturbations in efficiency DMUs and calculate its stability with spatial distances, providing a necessary and sufficient condition for such perturbations to affect the results of calculations of other efficient DMU super-efficiencies. A new super-efficiency model is proposed, which combines spatial distance to increase the constraint on projection point. Numerical examples are used to illustrate the model. On this basis, a spatial distance model for calculating inefficient DMU efficiency is further developed.
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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 | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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 it