Reflections on the Recommendations of the Task Force to Modernize Securities Legislation in Canada: A Retail Investor Perspective
Bibliographic record
Abstract
In 2005, the Investment Dealers Association of Canada funded a task force to review and recommend changes in securities legislation in order to promote more effective Canadian capital markets.An important factor motivating the establishment of the Task Force was the observation in the academic literature that Canada had a higher cost of capital than other countries after accounting for risk.This "Canada Discount" meant that Canadian companies had to pay more for capital, and thus investment decisions were negatively affected.The issue that the Task Force was interested in pursuing was the extent to which changes in regulation could reduce or eliminate the discount, or even shift the situation to a "Canada Premium."The Task Force to Modernize Securities Legislation in Canada deliberated until October 2006 when the report and recommendations were released.'Integral to the Task Force's deliberations was the funded research undertaken by academics and to a lesser extent, practitioners, from around the world.There were 30 research papers prepared on a range of topics.The final 65 recommendations were informed by this research.However, not all of the research topics were addressed in the recommendations.This research effort was unprecedented in Canada.
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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 | 0.018 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.025 | 0.016 |
| Scholarly communication | 0.029 | 0.008 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.059 | 0.048 |
| Insufficient payload (model declined to judge) | 0.012 | 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".