Estimating directional returns to scale in DEA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Data envelopment analysis (DEA) is one of the most commonly used methods to estimate the returns to scale (RTS) of the public sector (e.g. research institutions). Existing studies are all based on the traditional definition of RTS in economics and assume that multiple inputs or outputs change in the same proportion, which is the starting point to determining the qualitative and quantitative features of the RTS of decision-making units (DMUs). However, for more complex products, such as the scientific research in institutes, changes of inputs or outputs are often not in proportion. Therefore, the existing definition of RTS in the framework of DEA may need to be extended to estimate the RTS in such situations. This paper proposes the definitions of directional scale elasticity and directional RTS in the DEA framework and estimates the directional RTS using DEA models. Further in-depth analysis is performed for an illustrative example of 16 basic research institutes in the Chinese Academy of Sciences (CAS) in 2010.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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