Inverse regulation of DMUs efficiency through identifying hierarchical pathways with heterogeneous complementary indicators
Bibliographic record
Abstract
Data Envelopment Analysis (DEA) primarily serves as a benchmarking tool for measuring the relative efficiency of Decision-Making Units (DMUs). However, traditional DEA models suffer from screening measurement indicators that possess heterogeneous attributes and from the inability to quantitatively improve DMUs efficiency through relative efficiency calculations. Therefore, based on tier benchmarking management learning identification pathways, we design a research framework that considers heterogeneous complementary indicators to identify tiered paths for effectively regulating DMUs efficiency in reverse. First, incorporate heterogeneous complementary indicators alongside homogeneous ones to refine the production indicator measurement system. Additionally, the Context-dependent Slacks-Based Measure (SBM) model is introduced to divide the frontier into benchmark efficiency tiers. The A* algorithm is adapted to utilize the minimum relative efficiency difference as its heuristic function, enabling the identification of the optimal path through adjacent benchmark tiers. Finally, the ant colony optimization (ACO) algorithm is integrated with the Context-dependent SBM model to optimize the weighting coefficients of both homogeneous and heterogeneous production indicators within a non-radial framework. The research framework is validated using two case study datasets, with empirical results demonstrating its practical feasibility and superior algorithmic robustness.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 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".