Weathering the Storm: Taiwan, its Neighbors, and the Asian Financial Crisis.
Bibliographic record
Abstract
In July 1997, the promise of the economic miracle and the century devolved into economic chaos and the onset of what has become known as the Asian financial crisis. One by one, many of the region's great economic success stories suffered damage to their financial markets, their currencies, and economic well-being. This volume, the result of an April 1999 conference organized by the Chung-Hua Institution for Economic Research and the Brookings Institution, examines the sources and lessons of the Asian financial crisis. Experts from both sides of the Pacific have drawn valuable policy lessons from the failures and successes of four key economies in the region: Indonesia, South Korea, Thailand, and Taiwan. In examining Taiwan's relative success in weathering the storm, this volume helps explain the widely varying degrees of performance of the region's affected economies. The concluding chapter focuses on general principles for the liberalization of financial markets and stabilization of macroeconomy in developing countries. This work provides much-needed new understanding and reasoned policy lessons to help the Asia-Pacific region meet its vast economic potential. It will be useful for academics and economic policymakers in governments, international organizations, universities, and research institutions, both in the region and beyond, as they assess and implement strategies for more stable regional and global economic 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".