The International Financial Crisis and Policy Challenges in Asia and the Pacific
Bibliographic record
Abstract
This volume is a collection of the speeches, presentations and papers from a conference on International Financial Crisis and Policy Challenges in Asia and the Pacific. The event was co-hosted by the People's Bank of China (PBC) and the Bank for International Settlements (BIS) to mark the formal completion of the BIS Asian Research Programme. It was held on 6-8 August 2009 in Shanghai, China, at the same location where the establishment of the Programme was first announced in 2006. Senior officials from all 12 Asian Consultative Council (ACC) central banks, as well as academic scholars and economists from the BIS Representative Office for Asia and the Pacific attended the conference. The formal addresses included speeches by Jaime Caruana, General Manager of the BIS, Zeti Akhtar Aziz, Chair of the ACC and Governor of the Central Bank of Malaysia, and Zhou Xiaochuan, Governor of the PBC. The conference provided an opportunity to re-examine the existing approaches to preserving monetary and financial stability in Asia and the Pacific, in the light of the lessons of the international financial crisis. Even though the impact of the crisis was more muted in this region than elsewhere, there are many lessons to be learned.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".