Efficiency evaluation for decision making units with fixed-sum outputs using data envelopment analysis and stochastic multicriteria acceptability analysis
Bibliographic record
Abstract
The generalized equilibrium efficient frontier data envelopment analysis (GEEFDEA) approach, an extension of the DEA method, has been widely used to solve the problem of evaluating decision making units (DMUs) producing fixed-sum outputs. It constructs a common equilibrium efficient frontier through a minimum reduction strategy for fixed-sum outputs and uses this frontier as a benchmark to achieve a complete ranking of DMUs. However, the existence of multiple feasible equilibrium efficient frontiers may lead to inconsistency in the evaluation criteria, and this possibility limits the method’s usefulness. In this paper, an integrated framework for solving this problem is proposed to rank DMUs by using stochastic multicriteria acceptability analysis (SMAA-2) method combined with the GEEFDEA approach. Instead of using a certain common equilibrium efficient frontier as in conventional GEEFDEA approaches, we explore all possible frontiers to answer various robustness questions by computing rank acceptability indices and pairwise winning indices. Furthermore, we derive the complete ranking from the dominance relationships among the DMUs. Two numerical examples are used to demonstrate the effectiveness and rationality of the proposed hybrid approach.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| 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".