Research on performance evaluation of university science and technology achievement transformation from the perspective of fuzzy indicators
Bibliographic record
Abstract
This paper develops a comprehensive evaluation index system designed to measure the performance of scientific and technological achievements transformation in colleges and universities. Recognizing the inherent fuzziness and uncertainty in performance indicators, the study introduces a novel evaluation approach that integrates fuzzy logic with the TOPSIS decision-making method, enhanced through the IVPFOWA (Interval-Valued Pythagorean Fuzzy Ordered Weighted Averaging) operator. The IVPFOWA operator is first defined to establish the theoretical foundation for handling fuzzy attribute data. Building on this, the paper outlines the detailed steps of the TOPSIS evaluation decision process when combined with the IVPFOWA operator, demonstrating how the method can effectively address ambiguity in performance assessment. To validate the practicality and effectiveness of the proposed framework, Jiangsu University of Science and Technology is selected as a case study. The results confirm that the method provides a more reliable and adaptable tool for evaluating transformation performance under complex and uncertain conditions.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".