Transnational R&D Centers and National Innovation Systems in Host Countries: Empirical Evidence from China
Bibliographic record
Abstract
With globalization of research and development (R&D), an increasing number of transnational R&D centers have been established in developing countries, including China. However, how these transnational R&D centers affect the host country's national innovation system (NIS) is still not clear. Building on the literature, this article puts forward hypotheses about transnational R&D centers' embedment process, which is gradually adaptive, cooperative, and dynamic. It then tests the hypotheses by means of a unique survey on transnational R&D centers in China. The results indicate that the embedding of transnational R&D centers has positive effects on the host country's NIS. Relational embedding, structural embedding, and virtual embedding have different influences with different lag periods of effects; the first two modes are the most influential. Transnational R&D centers in China still remain at the early stage of embedding. This article suggests that the Chinese government should take several measures to maximize the positive influences of transnational R&D centers on China's 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| 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".