Semi-Additive Integer-Valued Production Technology for Analyzing Public Hospitals in Mashhad
Why this work is in the frame
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Bibliographic record
Abstract
Conventional Data Envelopment Analysis (DEA) models assume real-valued input-output data and ignore the collaboration among decision-making units (DMUs) in the analysis of efficiency. This paper proposes a novel DEA production technology that is capable of dealing with the collaboration concept and resource sharing for both integer and real-valued data in the efficiency measurement and target setting. This is accomplished by way of a mixed integer linear programming (MILP) efficiency analysis model. We also deal with the computational aspect of the proposed model and invent a MILP with the computational complexity of [Formula: see text] rather than [Formula: see text]. We explain the proposed models by numerical examples and graphical illustrations. We apply our models for efficiency analysis of 15 governmental (public) hospitals in Mashhad City in Iran and demonstrate the practical relevance and advanced future of the proposed methods.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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