A comparative analysis of the Australian and New Zealand liquidation schemes
Bibliographic record
Abstract
Abstract On September 28, 2022, Australia announced an inquiry into the effectiveness of its corporate insolvency laws. The Australia and New Zealand corporate insolvency frameworks have similar objectives and operate in a similar context where, as is the case the world over, most companies are small to medium enterprises. Despite liquidation being just one of several collective and formal corporate insolvency procedures, it is the most frequently occurring procedure in both countries by a large margin. The Australian and New Zealand liquidation schemes have many similarities but also some key differences. Differences include the structure of the respective schemes; the levers prompting liquidation of companies in appropriate circumstances; the role of creditors, the court and the regulator; and the management of low‐value and assetless liquidations. These differences are analysed to determine what, if anything, the New Zealand scheme might contribute to development and/or reform of Australian corporate insolvency law. As consistency and coordination with Australian insolvency law is a New Zealand policy aim, the lessons the Australian scheme might have for New Zealand are also considered. Many of the points on which the Australian and New Zealand liquidation schemes differ are of universal concern (such as the management of low‐value liquidations), meaning that the nature and success (or otherwise) of the Australian and New Zealand responses are of wider, comparative interest.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".