Guidelines for Improving the Budget Performance Management of Public Universities in Guangdong
Bibliographic record
Abstract
The objectives of this research were: 1) to study the current situation of budget performance management of public universities in Guangdong; 2) to investigate the guidelines for improving the budget performance management of public universities in Guangdong; 3) to evaluate the adaptability and feasibility of guidelines for improving the budget performance management of public universities in Guangdong.The sample group of this research was 285 administrators in public universities in Guangdong. They were selected by systematic random sampling and sample random sampling. The interview group was ten administrators from seven representative universities in Guangdong. The experts for evaluating the adaptability and feasibility of guidelines for improving budget performance management consisted of high-level administrators from each usual public university, totaling seven people. Research instruments included 1) a questionnaire, 2) a structured interview, and 3) an evaluation form—data analysis using percentage, mean, standard deviation, and content analysis.The results were found that: 1) The overall level of budget performance management of public universities in Guangdong is high, but it also reflects many problems; the main issues were as follows: (1)There were widespread challenges in budget performance goal management within universities, (2) including incomplete performance execution tracking and monitoring management, (3) insufficient strategic alignment of performance evaluation management, and (4) difficulties in applying performance evaluation results in feedback and application management, 2) The guidelines for improving the budget performance management divided into three aspects, which including (1) budget organization, (2)budget process, and (3)information system; and 3) The adaptability and feasibility of the guidelines for improving the budget performance management of universities were at the highest level.
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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 | 0.033 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".