THE EVOLUTION OF INSOLVENCIES IN CENTRAL AND EAST EUROPE
Bibliographic record
Abstract
Vera Jurova during the „Insolvency Law in Europe – Giving people and businesses a second chance” Conference held in Jurmala – Letonia (23rd of April 2015), declared that following the financial crisis, the number of bankruptcy / insolvency broke out in all State Members. Although, at the moment, the trend is to stabilize, their number is much higher than before the crisis. This states, than within the European Union, half of the new created companies do not survive the first 5 years of activity and daily, 600 companies go bankrupt, which indicates that the number of annual bankruptcies rises to 200.000 companies. These figures must not be seen regionally at the European Union’s level as, because of the interconnections of the single market, the companies operate across borders (a quarter of them as stated by Vera Jurova) and the difficulties of one are chained transmitted towards the commercial partner companies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".