Bibliographic record
Abstract
In the last few years the US and Canada have suffered low rates of economic growth and associated higher levels of unemployment. These problems seem to have reinvigorated a push by industry, governments and policy makers to consider ways to foster the growth of businesses, and, in particular, small to medium enterprises (SMEs). For example, in 2012 the US Congress passed legislation to amend the Securities Act 1933 to ease restrictions on the capital raising activities by SMEs. Some Canadian regulators are also considering whether restrictions on capital raising by SMEs should be relaxed by, for example, expanding the existing offering memorandum prospectus exemption to all provinces. This exemption, permitted in some, but not all, Canadian provinces, provides a method by which SMEs can raise capital by issuing securities without complying with the more onerous requirements of preparing and filing a prospectus. Given this current debate about whether restrictions on capital raising activities by SMEs should be eased, this article examines the history of this offering memorandum prospectus exemption in Canada, and, drawing upon some data filed with a number of Canadian securities regulators, considers how it is currently being utilized.
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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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.025 | 0.010 |
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".