Research on performance evaluation of university science and technology achievement transformation from the perspective of fuzzy indicators
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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