The Australian corporate rescue regime: bold experiment or sensible policy?
Bibliographic record
Abstract
Abstract This paper takes its title from a paper given by the Honourable Justice Robert Austin, of the Supreme Court of New South Wales, to a conference on Key developments in Corporate Law and Equity in March 2001. In that speech he described Australia's corporate rescue regime as a “bold experiment”. This paper suggests that this is not a justified description and further that it is unlikely to end in the foreseeable future. The paper consists of a broad outline of how the system operates in Australia. It provides some commentary on the more significant features of the operation of Part 5.3A of the Corporations Law and considers suggestions that have been made in respect of reform of the legislation. The paper goes on to consider how the regime has been used since its introduction showing it is now the most widely used form of insolvency administration. The paper then examines briefly some of the attempts at evaluation of the regime. It concludes by suggesting that at this stage there is inadequate information to be conclusive as to the procedure's success or otherwise in fulfilling its aims of providing better returns to creditors. It is argued first, that the wide use of the procedure suggests that it is unlikely to be fundamentally altered in the near future. A further conclusion is that there is some soundness in the approach that the legislation takes in having less court control and a greater role played by the insolvency practitioner.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".