China's Cross-Regional Fta Initiatives: Towards Comprehensive National Power
Bibliographic record
Abstract
Introduction Since the late 1980s and the advent of reforms led by Deng Xiaoping, China has pursued an increasingly export-oriented trade policy, accompanied by selective domestic agricultural and industrial restructuring, privatization and internationalization.1 But while China has long been a vigorous global trader and has entered into numerous trade and economic agreements with partners around the world, Beijing's leaders are relative newcomers to free trade agreements (FTAs) . This is particularly so compared with counterparts in Europe, North America, Australia and New Zealand, and some of China's Asian neighbours such as South Korea, Thailand and Singapore. For example, when Pangestu and Gooptu in mid-2003 listed 36 Asian FTAs completed or contemplated, that list included twelve entries involving Singapore, ten involving South Korea, and five involving Japan, plus more than a half-dozen arrangements involving Thailand, Hong Kong, Taiwan and the Association of South East Asian Nations (ASEAN) group. However, China appeared only twice on the list, once regarding a possible China-ASEAN FTA and the other time regarding the even looser ASEAN Plus Three discussions.2 No bilateral FTA was on China's agenda at that time.3
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".