CHINESE EXPERIENCE WITH GLOBAL 3G STANDARD-SETTING
Bibliographic record
Abstract
China's growth strategy as set out in the 11th 5-year plan in 2005 called for upgrading of product quality, the development of an innovation society, and reduced reliance on foreign intellectual property with high license fees. Consistent with this policy, China has been involved in recent years with the development of a Chinese standard in third generation (3G) mobile phone technology, both in negotiating the standard and seeing it through to commercialization. This is the first case of a developing country both originating and successfully negotiating a telecommunications standard and this experience raises issues for China's future development strategy based on product and process upgrading in manufacturing. We argue that while precedent setting from an international negotiating point of view, the experience has thus far is unproven commercially. But the lessons learned will benefit future related efforts in follow-on technologies if similar Chinese efforts are made. This paper documents Chinese standard-setting efforts from proposal submission to ITU to the current large-scale trial network deployment in China and overseas trial networks deployment. We discuss the underlying objectives for this initiative, evaluate its effectiveness, and assess its broader implications for Chinese development policy.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".