Applying fuzzy delphi and best-worst method for identifying and prioritizing key factors affecting on university-industry collaboration
Bibliographic record
Abstract
The collaboration between the universities and industries is currently in the focus of attention globally. Governments, universities, and industries are interested in good and effective collaboration, which would be beneficial for all parties. To foster University-Industry Collaboration, and to help transfer the knowledge and technology between these two parties, academics, politicians and companies are paying attention to science and technology policies more than ever. In this study, the factors affecting the improvement of University-Industry Collaboration are identified and prioritized. In the first step, 20 factors are identified and 12 factors are selected using the Fuzzy Delphi method. Then, using the BWM method, prioritizing the extracted factors is determined for industry sponsorship of the university research. Finally, based on the results, the discussion is conducted and six major strategies are presented to improve this relationship.
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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 | 0.051 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".