Bankruptcy Law Severity for Debtors: Comparative Analysis Among Selected Countries
Bibliographic record
Abstract
Purpose: The objective of this paper is to propose the new indicator of bankruptcy law severity for debtors (BLSI-Bankruptcy Law Severity Index). On the basis of this index we conducted comparative analysis of debtor/creditor friendliness of bankruptcy laws among 27 selected countries. Design/Methodology/Approach: In the research the following methods were used: analysis of legal acts, literature review and expert method. Findings: The empirical results show that the most debtor-friendly bankruptcy and restructuring laws are those of the USA, Ireland and Canada. At the opposite pole were Slovenia, Australia and Austria. It can also be noted that many EU countries have a similar level of BLSI measure, which is most likely a consequence of harmonisation activities undertaken within the Community. Practical Implications: The conducted research enables us to propose the direction of changes in bankruptcy and restructuring laws in the next stage. Originality/value: On the basis of proposed BLSI, we will be able to examine the relationship between the severity of bankruptcy law and innovation, entrepreneurship and the level of development of financial markets in the studied 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".