Emerging Asia: Two Paths through the Storm
Bibliographic record
Abstract
The overall effect of the global financial crisis on emerging Asia was limited and short-lived. However, the crisis affected some countries in the region more than others. Two main crisis transmission channels, exposure to U.S. financial markets and reliance on manufacturing exports, determined how severely countries in the region were affected. Countries that were relatively less connected to global financial markets and relied less on trade fared better and recovered more quickly than countries that were more dependent on global financial and trade markets. When the global financial crisis erupted in the summer of 2007, its spread was limited initially to the financial sectors of the United States and other advanced economies. After Lehman Brothers collapsed in the third quarter of 2008, the crisis became more widespread. During this time, the gross domestic product of the United States and western European countries fell from pre-crisis growth rates of around 3 % to below zero. Yet, many emerging Asian countries were relatively unaffected, with annual GDP exceeding 5%. Most of the region did not feel the effects of the financial crisis until late 2008 and early 2009. Even after that, the impact on the region overall was less severe than on the rest of the world. Most countries bounced back relatively quickly (Tille 2011, Goldstein and Xie 2009).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".