ASEM and the "Cinderella Complex" of EU–East Asia Economic Relations
Bibliographic record
Abstract
Within the family of Triad power-regions (North America, Europe, East Asia), the Eurasian economic axis persists as the poor third relation in comparison to its trans-atlantic and transpacific counterparts. This study examines the nature of the "Cinderella complex" that besets EU-East Asia economic relations and the structural constraints within the Triadic political economy that impede its resolution. It more specifically considers what role can be played by the Asia-Europe Meetings (ASEM) inter-regional framework in fortifying the Eurasian economic relationship. Moreover, the capacity of ASEM to develop its geo-strategic and multilateral utility is a core theme of this article. It is proposed that at the millennial eve, ASEM has missed various opportunities both to enhance its salience and to take bolder initiatives in the Eurasian co-management of the post-hegemonic world order. Such passivity was most clearly revealed in ASEM's handling of the 1997-98 East Asian financial crisis. Thus, as it currently stands, ASEM possesses a limited capacity to significantly redress the structural imbalances in the Triadic political economy: a far more substantive ASEM agenda is required to fulfil its potential geo-strategic and multilateral utility. This study notes that proposals carried in the first report of the recently established ASEM Vision Group would make a significant initial contribution towards this end.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".