Research on Performance Evaluation and Appraisal Methods of Asset Management in Public Universities Introducing Super-Efficient DEA Modeling
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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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".