Governance Complexities in Firms with Dual Class Shares
Bibliographic record
Abstract
In a typical public company, shareholders can elect the board,appoint auditors, and approve fundamental changes. Firms with dual class share (DCS) structures alter this balance by inviting the subordinate shareholders to carry the financial risk of investing in the corporation without providing them with the corresponding power to elect the board or exercise other fundamental voting rights. This article fills a conspicuous gap in the scholarly literature by providing empirical data regarding the governance of DCS firms beyond the presence of sunrise and sunset provisions. The summary data suggest that the governance of DCS firms is variable. A large proportion of DCS firms have no majority of the minority voting provisions and no independent chair. By contrast, almost half of the DCS firms have a sunset clause and a majority of independent directors. Finally, just under one-third of DCS firms have change of control provisions over and above existing law. On the basis of this evidence, this article argues against complete private ordering in favor of limited reforms to protect shareholders in DCS firms including: mandatory sunset provisions, disclosure relating to shareholder votes, and buy out protections that would address weaknesses inherent in DCS firms.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".