Bibliographic record
Abstract
INTRODUCTION Over the last two decades, most industrialised and developing countries have experienced episodes of boom and bust in credit markets. These credit cycles often coincided with cycles in economic activity and asset prices. The unwinding of the imbalances built up in the boom has, in some cases, led to severe problems in the financial sector, sometimes culminating in an outright banking crisis. In Japan, the second biggest economy of the world, asset price deflations, both in equity and property, were followed by a decade of financial fragility and deflationary developments in goods prices, with consumer prices falling continuously after 1999.With short-term interest rates having reached the zero lower bound, the country appears to be trapped in a deflationary spiral out of which it finds itself unable to escape. Other South-east Asian countries, such as Hong Kong and Singapore, have also experienced asset price deflations followed by a marked drop in credit creation and goods price deflation in recent years. Some commentators argue that the United States and other industrialised countries are also now, in the wake of the worldwide slump in share prices, on the brink of deflation. Both the experience from historical episodes of financial crisis in the late nineteenth and early twentieth centuries and from recent boom–bust cycles in credit markets, suggest that consumer prices respond with a lag to developments in credit markets. Consumer price inflation is often low or falling during credit booms and peaks after the onset of the bust. Asset prices, especially property prices, on the other hand, appear to follow closely behind or even to lead bank lending.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".