China's Leadership in the World Ict Industry: A Successful Story of Its "Attracting-in" and "Walking-out" Strategy for the Development of High-Tech Industries?
Bibliographic record
Abstract
of the most striking phenomena after China's three decades of is that the country's huge volume of exports is increasingly in the high-tech field, and a number of large domestic enterprises are approaching multinational stages, operating worldwide and acquiring firms in advanced economies. It seems that China is now making a leap from a simple manufacturing centre to an advanced technology superstate.2 How did China achieve this success? Is the growth really as impressive as it appears? Has China become more competitive and taken a lead in some high-tech industries? Previous literature has documented the motivations, regulatory changes and development process of China's opening up in promoting expansion of trade and outward investment.3 What is still little known is how the attracting-in (Yinjinlai) and walking-out (Zouchuqu) strategies have been used to develop China's competitiveness and to catch with leading countries in some high-tech sectors. This paper attempts to analyze how these policies have been applied to one of China's priority development
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".