Bibliographic record
Abstract
In 2010, Magna International Inc. (Magna) obtained court approval of an arrangement to buy back its super-voting shares, which placed control in the hands of a shareholder with 0.6 per cent of the equity, at a 1,800 per cent premium to non-voting shares. I agree with the decision to approve but disagree with some of the court's reasons. Magna's board failed to provide a clear description of the possible benefits of the transaction. For example, theory and empirical analysis challenge the board's suggestion that liquidity benefits would help justify the arrangement. The board and the court also failed to describe clearly the beneficiaries of the transaction. A special committee of the board concluded that Magna would benefit from the arrangement but offered no conclusion on whether shareholders would benefit. This is internally inconsistent: Since Magna issued shares as consideration in the arrangement, the only way to determine whether Magna would benefit on net was to determine the arrangement's impact on share value. I analyze these and other errors, identify the possible benefits and beneficiaries of the arrangement, and conclude that while the court could have been more critical of Magna's approach, it was correct to look to shareholder support-both from market signals and from voting-as a justification for approving the arrangement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.030 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.017 | 0.019 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".