Research on Performance Evaluation and Appraisal Methods of Asset Management in Public Universities Introducing Super-Efficient DEA Modeling
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
A scientific, comprehensive and effective evaluation system of asset management performance of public colleges and universities in the context of high-quality development in the new era contributes to the "asset power" for the construction of high-level and high-quality development of colleges and universities.This paper takes 20 public colleges and universities in Province Y as research samples, and analyzes the asset management performance of public colleges and universities and its influencing factors through the super-efficiency DEA model and SFA model.The results show that the asset management performance of 13 public colleges and universities has reached DEA effective, and the rest of them are DEA ineffective.Human and material inputs have a significant positive effect on the asset management performance of public universities in terms of inputs, and both research income and number of patents have a significant positive effect on the asset management performance of public universities in terms of outputs at the 1% level.Relying on the scientific evaluation index system of asset management performance of public universities, establishing a high-level asset management team and clear budgeting and audit management are effective means to improve the asset management performance of public universities.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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