Bibliographic record
Abstract
In recent years, Extreme Value Theory (EVT) has been proposed to deal with the heavy tailed distributions. This paper introduces L-moments and L-moment ratios based on EVT to analyze the distributional characteristics of exchange rates, and furthermore introduce the Kappa (κ) distribution to analyze the effects of globalization by understanding differences and similarities among Asian countries and developed countries before and after the crisis. We classify the behavior of exchange rates of East Asian countries and several financially developed countries into groups: the EURO zone, UK, Japan and some Asian countries. These entire groups have experienced the same or similar shocks during credit crunch in 2008; however the responses to the event for each group are different. We take extreme value point of view to analyze the effects of globalization by examining the exchange rates. For this purpose, we calculate the so called L-moments and L-moment ratios. Based on these estimates, we implement structural break test based on Kappa distribution showing the different aspects of the analyses. The most striking features are the different shape of L-moments and the coefficients of κ distribution among each group, and a closer examination of the
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".