Enforcement Issues Associated with Prospectus Exemptions in Canada
Bibliographic record
Abstract
This paper examines the regime of prospectus exemptions in Canada. The focus is on the enforcement process, and to this end the article includes an empirical examination of enforcement actions by both the securities regulators and the Investment Industry Regulatory Organization of Canada (IIROC). The end in view is two-fold; to provide a description of the exempt market and enforcement efforts, and to comment on whether enforcement resources are properly deployed. The latter necessarily requires a critical examination of the nature of the various exemptions, their purposes, and whether they are appropriately crafted to achieve their desired ends. That in turn requires an analysis of which issuers use the various exemptions, what types of securities are issued, and who the buyers are. Unfortunately, the Canadian data falls far short of painting a comprehensive or reliable picture of the exempt market. This is a product of many factors. Notably, many exempt financings need not be reported to the regulators. Moreover, many small private companies do not report their exempt financings even when required to do so. The extent of the under-reporting problem (from both causes) is illustrated by data from Statistics Canada suggesting that in 2014, there were about 156,000 exempt financings across Canada. However, in that year, only about 7,125 exempt financing reports were filed with the regulators. Understanding the nature of the exempt market is also hindered by the fact that not all of the provincial regulators compile statistics relating to the use of the various exemptions, and even among regulators that provide statistics, these are not published on an annual basis. Added to this, the published statistics that we have fall far short of painting a comprehensive or picture of the exempt market. The regulators do not compile cross-tabulated statistics relating to the nature of the issuers, the types of purchasers, the amount of capital raised, and the types of securities issued. Nor do they keep statistics on redemptions of prospectus-exempt securities, thus potentially yielding a material overstatement of the net amount of prospectus exempt financings each year.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".