Aggregation of meta-technology ratio in DEA framework using the evidential reasoning approach
Bibliographic record
Abstract
The metafrontier data envelopment analysis (DEA) model is a popular evaluation technique when different decision-making units (DMUs) may exhibit production technology heterogeneity. In this framework, the group metatechnology ratio (MTR) is an important indicator to help measure the technology gap at the group level. The common approach to the group MTR aggregation is the arithmetic average approach, which requires the MTR of the DMU to satisfy the ‘additive independence’ condition. Because the MTRs are generated from the same data set and linked to each other, the MTRs do not meet the ‘additive independence’ condition. Therefore, a new aggregation approach is needed. This study applies the evidential reasoning (ER) approach to aggregate the group MTR by the transformation of the MTR of DMU to pieces of evidence. Moreover, this study proposes examples that verify the applicability and practicality of the MTR aggregation using the ER approach, including an empirical example of the evaluation practice of the transportation system of 30 provincial regions in mainland China for 2013–2019.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".