Bibliographic record
Abstract
Purpose – The purpose of this study is to analyze the aggravating and mitigating factors considered by the Investment Dealer Association (IDA)’s (Now IIROC) hearing panels when determining penalties. Design/methodology/approach – To conduct this research, Quicklaw’s database Securities Regulation Tribunal Decisions were searched for all decisions made by the IDA between January of 2003 and June of 2008. This paper analyzes the 238 cases that were found. Findings – The findings revealed that the IDA’s hearing panels were more likely to identify mitigating rather than aggravating factors when considering the appropriate penalties to be imposed on registrants. Perhaps this was because the hearing panels were more preoccupied with identifying mitigating factors that would, in turn, lead to less severe penalties for their members. The aggravating factors identified and considered were fewer in number than the aggravating factors identified but not considered by the hearing panels when imposing penalties. Research limitations/implications – IIROC needs to take stock of this study and encourage hearing panels to seriously take into consideration the factors listed in their sanction guidelines and apply them methodologically to each case. Originality/value – Despite the widespread use of self-regulatory organization (SROs) to regulate various occupations, SROs remain an understudied institution. This is the first study of its kind that looks at the aggravating and mitigating factors used by an SRO’s hearing panel in administrative hearings.
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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.010 | 0.001 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.007 |
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".