Global Supply Chains as Drivers of Innovation in China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract China’s manufacturing and innovation capabilities are directly related. Availability of complementary resources in rapid prototyping, test production, and components and the ability to deploy innovations at scale increasingly lead high-technology firms, including startups, to consider China as a developmental base across sectors from big data to cloud computing, smart grid, renewable energy, and alternative energy vehicles. Entry into global value chains (GVCs) has led to vast transfers of knowledge, creating human resource capabilities that continuously facilitate the upgrading of Chinese firms. China’s most advanced industries were all those characterized by active participation in GVCs. China’s entry into GVCs has differed significantly from the experiences of other emerging economies, arguably affording China greater innovation benefits. This is directly related to China’s institutional environment of “structured uncertainty.” Structured uncertainty shaped the pattern and impact of entry into GVCs, dictating which regions entered GVCs, when, and how, with long-term knowledge transfer effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
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 it