Bibliographic record
Abstract
INTRODUCTION Economists agree that the long-term growth of living standards depends on the capacity of an economy to sustain technological progress, whether by adopting technologies from abroad, through its own technological innovations, or, most likely, through a combination of adoption and innovation. The purpose of this chapter is to describe and analyze China's science and technology (S&T) capabilities and the economic, institutional, and policy context that together are shaping the range and growth of these capabilities. We conduct this analysis against the background of a large and fast-growing literature on the subject. China's national innovation system is making two transitions – from plan to market as it moves away from a centrally directed innovation system and also from low-income developing country toward Organisation for Economic Co-Operation and Development (OECD) industrialized country status as it intensifies its innovation effort and more effectively deploys the ensuing technological gains. Many of the impulses and policies of China's current S&T system are legacies of the nation's traditional economy going back to the nineteenth century and before. These include the recognition that access to Western S&T is critical to China's economic modernization and the consequent openness to foreign technology, advisors, and investment, particularly in special zones in the coastal areas.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 source (direct Gemma or distilled Codex), 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".