Corporate Law Federalism in Historical Context: Comparing Canada and the United States
Bibliographic record
Abstract
Although American and Canadian corporate law share many similarities, they are also marked by important institutional differences. Among the most notable are the differing roles of federal versus state/provincial policymaking in the two countries: While American corporate law has been deeply influenced by jurisdictional competition among the states, Canadian law has instead been shaped by federal legislative activity, as seen today in the standardizing influence of the Canada Business Corporations Act. These different institutional histories have led to distinct evolutionary paths, with important substantive consequences for contemporary corporate law. Despite considerable academic attention to the subject of corporate law federalism, these historical differences between Canada and the United States are not well understood. Drawing on historical evidence, this article explains why jurisdictional competition arose in the United States but not Canada by examining the "Great Merger Movement" of the late nineteenth and early twentieth centuries. Specifically, this article makes three related arguments: (1) In the United States, the rise of jurisdictional competition was driven not by corporate governance issues, as is often assumed, but rather by the desire to avoid state and federal antitrust restrictions; (2) For a variety of reasons, cartelization and price fixing were more viable in Canada than the United States, delaying the onset of consolidative mergers; and (3) When the Canadian merger movement finally arrived, Canadian federal company law readily facilitated industrial consolidation, reducing the incentives for individual provinces to compete to attract company charters. Given the permissiveness of Canadian company law and the availability of a federal statute, there was little demand for provincial governments to liberalize their own companies acts. In this respect, the different experiences of Canada and the United States reveal an intriguing historical irony--while Canadian corporate law is sometimes criticized as lacking in competitive responsiveness, the roots of this complacency are closely tied to the turn-of-the-century merger movement, in which Canadian law was more permissive than its traditional American counterpart.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.023 | 0.010 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".