Evaluating National Innovation System of Malaysia Based on University-industry Research Collaboration: A System Thinking Approach
Bibliographic record
Abstract
National innovation system (NIS) is an essential, effective and dynamic factor for the development of the nation. Despite the enormous research activities on NIS, there have been certain issues still remaining in this domain specially, in developing countries. Several articles have been published to deal with the factors affecting NIS of Malaysia but the role of research collaboration between university and industry (UIRC) on NIS is totally unattended in mainstream literature. Thus, the objective of this study is to determine the influence of UIRC on the NIS of Malaysia. Additionally, this study aims to indicate the current innovation performance and to identify the existing constraints of NIS. For the purpose of system modeling, system thinking approach is used to conceptualize and analyze the effect of UIRC on NIS. The result of this research shows that constraints of UIRC in Malaysia negatively influence on the success of NIS.
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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 | 0.011 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.013 |
| 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".