An empirical study of multi-factor decision tree analysis in the optimization of university management evaluation system
Bibliographic record
Abstract
Quality management is one of the factors determining the running level of a university, so it is necessary to evaluate university management scientifically and comprehensively. In this paper, a university management evaluation index system is constructed and optimized with reference to the multi-factor decision tree technology, and the cause degree and centrality degree are calculated through the DEMATEL method model, the causality diagram is established, and the ISM hierarchical structure analysis is subsequently carried out. The management influences with high centrality degree are service ability, student quality, employment, school size and presidential leadership. Through the results of the reachable matrix, four levels of college management influencing factors are divided, and it is found that the fundamental factors affecting the management level of colleges and universities are concentrated in the socio-economic and cultural level, the deeper factors are mainly the school’s own scale, funds and teachers and students, and the leadership quality and service ability are the superficial factors. Therefore, the improvement of university management evaluation system can be carried out with reference to the above levels and indicators.
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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 | 0.019 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| 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".