Does Convergence in Regulation Lead to Convergence in Practice? The Case of Dissident Proxy Contests in Canada
Bibliographic record
Abstract
In this chapter we look at the question of convergence in corporate governance by evaluating dissident proxy proposals in Canada. We build on earlier research on the dynamics of dissident proxy initiatives in the US (David, Bloom, and Hillman, 2007) and in Canada (Bates and Hennessy, 2010), with a detailed evaluation of the actors who file and respond to dissident proxy proposals. What does convergence mean in this context? From a regulatory and legal perspective, corporate governance in Canada is very similar to the US, with a shared Common Law heritage as former colonies of Great Britain. Yet important differences also exist. Canada has much smaller capital markets organized around provincial regulatory agencies. Publicly-held corporations in Canada are concentrated in fewer sectors, and large Canadian corporations are, on average, much smaller than large US corporations. Canadian regulations have enabled families to control corporations with supermajority voting shares. Founders and heirs who serve as executives are regularly featured in the Canadian business press, as are executives at large institutions. The discourse around corporate governance in the Canadian business press reflects the proximity of the US and exposure to print and broadcast media creating a ration-ale for convergence around codes for corporate governance (Enrione, Mazza, and Zerboni, 2006). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.038 | 0.036 |
| Scholarly communication | 0.019 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".