Key Findings from the ECRI Statistical Package 2012: Debt crisis hits Europe’s retail credit markets
Bibliographic record
Abstract
These Key Findings from the ECRI 2012 Statistical Package reveal that after a slender recovery of retail credit in 2010, European households registered a downward adjustment of their stock of loans in 2011. The deleveraging remains uneven and is only loosely related to the overall indebtedness of households to GDP. Moreover, the Euro Area and the EU appear to be decoupling into two groups in this regard: the geographic core and the periphery. Peripheral countries generally shared a period of fast credit expansion during the pre-crisis period, followed by record levels of deleveraging. Core countries, on the other hand, which registered low levels of credit expansion before the crisis, are now experiencing only moderate or no deleveraging. The Key Findings relate to the more detailed ECRI 2012 Statistical Package covering 38 countries: the 27 EU member states, three EU candidate countries (Croatia, Turkey and the Former Yugoslav Republic of Macedonia), the EFTA countries (Iceland, Liechtenstein, Norway and Switzerland) and four key global economies (the United States, Australia, Canada and Japan). The purpose of the package is to provide reliable statistical information that allows users to make meaningful comparisons between these countries.
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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 | 0.010 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 0.012 |
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".