An update on self-regulation in the Canadian securities industry (2009-2016)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose This paper aims to analyze the processing of complaints against investment advisors and Member firms through the Investment Industry Regulatory Organization of Canada (IIROC) enforcement system between 2009 and 2016. The paper used the misconduct funnel to show the number of complaints that are “funneled in,” and how these complaints are subsequently “funneled out” and “funneled away” at the investigation and prosecution stages of IIROC enforcement system. Design/methodology/approach The paper uses data from IIROC enforcement annual reports from 2009 to 2016. A combination of descriptive statistics and correlation matrices was used to analyze the data. Findings The findings indicate that while IIROC “funneled in” more complaints, a significant proportion of complaints were “funneled out” of its enforcement system and funneled “away” from the criminal justice system. Fines imposed were often not collected from individual offenders. IIROC, it seems, is ineffective in handling the more serious and systematic industry problems. Practical implications It is hard not to see the findings from this study being used by the provincial securities commissions and the federal government to support the call for a national securities regulator in Canada. Originality/value This is the first study of its kind to systematically analyze the enforcement performance of IIROC.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it