Directors' duties to prevent insolvent trading in a crisis: Responses to <scp>COVID</scp>‐19 in Australia and lessons from Germany
Bibliographic record
Abstract
Abstract This article considers relief from directors' duties to avoid trading whilst insolvent during the COVID‐19 pandemic in Australia and Germany. Comparative insolvency law literature traditionally compares Australia to jurisdictions such as the United Kingdom and New Zealand. However, Germany has a track record of using insolvency law to manage social and economic crises. The German approach suggests solutions to critical issues not dealt with in the Australian safe harbour legislation, such as the failure to suspend other statutory duties to provide clear guidance to directors on the balancing of various interests, and the treatment of potential voidable transactions and new monies (i.e., new funding or credit). The responses in both jurisdictions suggest a change in priorities away from creditor protection, a key raison d'être for these types of duties, during a crisis. Similar to the German approach of turning the obligation to file for formal insolvency proceedings off and on, the further safe harbour adopted in Australia as part of a new restructuring procedure for small businesses, which commenced on January 1, 2021, suggests that the use of safe harbours could become a permanent crisis‐management tool in Australia too, with potential consequences for the balance between debtor and creditor interests.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 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".