Modelling as a Tool for Cross-border Analysis of the Position of Insolvency Office Holders
Bibliographic record
Abstract
This paper presents a framework and a model applied to make a cross-border analysis of the position of Insolvency Office Holders. Both the framework and the model were developed in the course of an assignment to design Principles and Best Practices for Insolvency Office Holders for INSOL Europe. The framework is developed by induction from a variety of sources of rules and regulations regarding Insolvency Office Holders, while the model subsequently has been derived by deduction from the framework. Finally, the paper shows how this method assisted in determining the issues to be covered by Principles and Best Practices. The authors argue that commencing international legal comparison with abstract reasoning and modelling may lessen the effect of researcher's academic or professional blind spots and cultural bias and has the potential to enhance the value of cross-border analysis in terms of coherence, consistency and completeness. Copyright © 2016 INSOL International and John Wiley & Sons, Ltd
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".