Bibliographic record
Abstract
University-industry linkages (UILs) are not widely spread in Asian countries, but their extent is increasing, and firms tend to be satisfied with their interaction with them. As for the mode of UILs, in Asia, formal channels such as joint or contract-based research in Korea, China, and Malaysia and small-scale consulting in Thailand are more common, which is different from the case of the United States. This implies that different modes of UILs correspond to different stages of economic development of nations and/or the different capabilities of firms in each country. We also find that those that have certain R&D capabilities and thus conduct some R&D are the most frequent users of services from universities or public research institutes (PRIs). This implies that the relationship between R&D by firms and that by universities is more complementing than substituting. The fact that the firms that already conduct R&D activities tend to collaborate more with universities or PRIs might indicate the limitation of UILs as a new vehicle for catch-up. However, beyond the dichotomized question of supplementing or substitution, what matters more is apparently the absorption capacity of firms as well as the various (teaching, research, and entrepreneurial) capabilities of universities and laboratories. If such capabilities are there, there is no doubt that UILs will be more intense. Given the low or diverse degrees of capabilities of firms and universities in latecomer economies, increasing the level of their capabilities is foremost, followed by the utilization of diverse modes of UILs, depending on specific conditions and contexts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".