Bibliographic record
Abstract
This report has extracted cyclical patterns from economic indicators befitting the concept and characteristics of a financial cycle, calculated a composite index for the financial cycle and looked into Korea's financial cycle conditions. The composite financial cycle index was calculated based on whether the cyclical components' peaks are close to the times of financial unrest, and whether the indicators used for measuring the financial cycle are synchronized with each other. Based upon this we selected three indicators: the ratio of private credit to nominal GDP, real housing prices, and the share of non-core liabilities, standardized their cyclical components, and calculated the average. According to this Korea is found to have experienced five financial cycles since 1986. The average financial cycle length has been 23 quarters (5.8 years), longer than that of the real cycle (4.1 years). Korea has been in the expansionary phase of the fifth financial cycle since the fourth quarter of 2010. After having faltered for a bit due to the government's measures against household debt, the cycle has sustained its expansionary phase since 2014. Meanwhile, the synchronization of Korea's real economic and financial cycles had strengthened in the 2000s, but since the global financial crisis has now weakened.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".