Bibliographic record
Abstract
The purpose of this paper is to provide an empirical foundation from a capital markets perspective to ground the discussion and analysis on the constitutionality of a national securities regulator. Based on the data, the case for a national securities regulator for Canada is more evident now than it has ever been. This paper first explores the academic and empirical literature on the relationship between regulation and the strength of that jurisdiction’s capital markets, as measured by the cost of capital, liquidity and investor protection. Studies have found that Canadian companies have a higher cost of capital than their U.S. counterparts, even after accounting for risk, meaning that Canadian companies pay more financing than their peers. Canadian companies also receive lower valuations. This, in part, can be attributed to the limitations associated with our fragmented regulatory structure, as well as concerns about weak enforcement. The paper then explores the data on Canadian retail and institutional investors and their investing patterns, as well as the financing needs and preferences of Canadian businesses. The data show that the capital markets are now more important than ever to Canadian investors and businesses alike. Capital markets have become a preferred vehicle for investing the savings of individual Canadians, as compared to other investment opportunities. Similarly, institutional investors such as pension funds invest a significant proportion of their assets in the public capital markets. In terms of financing growth and expansion for Canadian businesses, capital markets play a more dominant role than they have ever done in the recent past. Finally, the paper explores how changes in the regulatory and global capital markets environment further exacerbate the negative impact of Canada’s fragmented regulatory system and highlight the need for national regulation.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".