Exempting 'fair value' transactions from the prohibition of collateral benefits in takeovers: Australia's approach
Bibliographic record
Abstract
The Australian Takeovers Panel recently released an Issues Paper inviting comment on numerous issues concerning the prohibition of 'collateral benefits' in s.623 of the Corporations Act. A key matter in the Issues Paper is whether benefits provided on 'fair value' terms should fall within the prohibition. This paper examines the equal opportunity principle which underlies s.623, including efficiency and other concerns with that principle, and the approach to collateral benefits in other jurisdictions, namely the US, UK and Canada. This paper then looks at the scope of s.623 and finds that courts and the Panel have become increasingly willing to exempt 'fair value' transactions from s.623, largely under the guise of its illusory 'different capacity' test. This approach to exempting 'fair value' transactions is in line with policy and efficiency concerns. Several reform measures are suggested, including introducing an express exemption for fair value transactions and a shareholder approval mechanism.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".