The Impact of Industry-University-Research Institute Cooperation on the Innovation Capability of Chinese University Faculty
Bibliographic record
Abstract
This paper aims to examine the impact of industry-university-research institute (IUR) cooperation on the cultivation of university teachers’ innovation abilities in Guizhou Province, China, through Triple Helix model and the National Innovation System theory. Through semi-structured interviews with 19 university teachers and 4 enterprise project leaders in Guizhou Province, and thematic analysis of the interview results, it was found that the six aspects influence how IUR cooperation affects the cultivation of university teachers’ innovation abilities in Guizhou Province: enterprise-driven collaborative research mechanisms, teacher characteristics and development, university-enterprise cooperation mechanisms and cultural differences, intellectual property rights and legal issues, resource support and innovation environment, and project innovation motivation and implementation constraints. This study provides an empirical basis for further implementing China’s innovation-driven development strategy and optimizing the IUR cooperation mechanism to enhance the cultivation of university teachers’ innovation abilities in Guizhou Province.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".