Bibliographic record
Abstract
Contents: PART I: NORTH AMERICA 1. Global Financial Crises Benton E. Gup 2. Spillover Effects from the US Financial Crises: Some Time-Series Evidence from National Stock Returns Apanard Penny Angkinand, James R. Barth, and Hyeongwoo Kim 3. Canadian Banks and the North American Housing Crisis James A. Brox PART II: EUROPE 4. The German Banking System and the Financial Crisis Horst Gischer and Peter Reichling 5. No Free Lunch-No Decoupling, The Crisis in Hungary: A Case Study Julia Kiraly and Katalin Mer PART III: ASIA AND AUSTRALIA 6. An Analysis of the Ripple Effects of the Global Financial Crises on the Korean Economy and the Recovery Jungeun Kim, Kyeong Pyo Ryu, and Doowoo Nam 7. Propagation of the US Housing Market Crisis into Asia: Impacts and Depths Masanori Amano and Hikari Ishido 8. How Australia Survived the Global Financial Crisis Chris Bajada and Rowan Trayler PART IV: INTERNATIONAL REGULATORY ISSUES 9. A Single Financial Market and Multiple Safety Net Regulators: The Case of the European Union Maria J. Nieto 10. The Global Financial Crisis: Back to Basics, Bank Supervision in Developing Countries Thomas Lutton and Joseph Cauthen 11. Hedge Funds and Offshore Financial Centers: New Challenges for the Regulation of Systemic Risks Navin Beekarry
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".