The <scp>Italian</scp> exclusion of farming enterprises from major insolvency proceedings: An assessment of its appropriateness within the <scp>European Union</scp> insolvency context
Bibliographic record
Abstract
Abstract The article examines the Italian approach to farming enterprises' insolvency. In Italy, farmers were traditionally excluded from the application of insolvency proceedings regardless of their corporate status. In the last decade, they have gained limited access to special insolvency procedures developed for consumers and small enterprises. The study seeks to compare the Italian perspective with the insolvency frameworks of the other European Union (EU) member states and place the Italian approach within the broader EU insolvency framework. The article questions the validity of the Italian exclusion of farmers from the major insolvency proceedings in light of the modern rationales of insolvency law (i.e., restructuring, rescuing and second chances). In doing so, the article has a fourfold structure. First, the article analyses the current Italian approach to the insolvency of farming enterprises. Second, it compares the Italian approach to the regimes concerning farmers' access to insolvency proceedings of the other 26 EU member states. Third, it analyses the EU insolvency framework concerning farmers' insolvency and evaluates the impact of the Directive on Restructuring and Insolvency on the rationale of the Italian insolvency regime. Last, the article seeks to put forward policy recommendations for future reforms of farmers' insolvency in Italy.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".