A bargaining game model for measuring performance of two-stage structures with a fixed‑sum output
Why this work is in the frame
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Bibliographic record
Abstract
As an important extension of the data envelopment analysis (DEA) model, two-stage fixed-sum output DEA models are used to measure the performance of two-stage structures where some of the outputs have a total constraint. It constructs a common equilibrium efficient frontier (EEF) through a minimum reduction strategy for fixed-sum outputs and uses this frontier as a benchmark to examine the efficiencies of two-stage structures. Since there may exist multiple EEFs in the second stage producing multiple stage efficiencies, this paper develops a Nash bargaining game model to measure the stage efficiency and overall efficiency. The efficiencies of the non-cooperative model for the two stages are used as the breakdown point and the unique bargaining efficiency scores for the two stages and the overall structure is obtained subsequently. The proposed model is applied to evaluate to evaluate the eco-efficiency of provincial industrial system in China.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| 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