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Record W2567899686

THE EVOLUTION OF INSOLVENCIES IN CENTRAL AND EAST EUROPE

2015· article· en· W2567899686 on OpenAlexaboutno aff
Bogdan Sticlosu Student, Bogdan Pîrvulescu Student

Bibliographic record

VenueThe Young Economists Journal · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Issues in Ukraine
Canadian institutionsnot available
Fundersnot available
KeywordsBankruptcyInsolvencyQuarter (Canadian coin)European unionState (computer science)BusinessFinancial crisisFinancial systemEconomyEconomicsFinanceEconomic policyGeographyKeynesian economics
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0040.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.036
GPT teacher head0.210
Teacher spread0.174 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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