East Asia and Pacific Economic Update, November 2009 : Transforming the Rebound into Recovery
Bibliographic record
Abstract
A vigorous economic rebound is under way in East Asia since the second quarter of 2009, following the sharp impact from the financial crisis and the global recession that began in late 2008. As much as the reduction in exports and industrial production across the region in the fourth quarter of 2008 and the first quarter of 2009 was unexpectedly swift and deep, so is the strength of the rebound, with doubts about green shoots dispelled in a matter of months and replaced by near-consensus views of a synchronized global rebound led by emerging East Asia. The robust rebound is due to a combination of timely and large fiscal and monetary stimulus in most countries in East Asia, notably in China, and a powerful process of inventory restocking that began after mid-2009. Globally, the advanced economies joined the rebound trend in the third quarter of 2009, and their contributions to global industrial production notably driven by inventory accumulation have begun to outpace the contribution from the East Asia region. These developments are set against a background of solid macroeconomic fundamentals, including high foreign exchange reserves, large private and corporate savings, and low corporate and government debt. The region's well-capitalized banks and much improved banking supervision since the 1997-98 Asian financial crisis have also helped limit financial contagion and the transmission of the forces of global recession.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.043 | 0.023 |
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".