Out of Place and Out of Line: Positioning the Police in the Regulation of Financial Markets
Bibliographic record
Abstract
In November of 2003, the Royal Canadian Mounted Police launched a major initiative to combat securities fraud in Canada. Spurred by the Enron scandals in the United States, this involved the establishment of a series of specialized white‐collar crime units with the express mandate of investigating serious cases of securities fraud and protecting investors from the worst of the market's abuses. After four years of activity, these units have produced little in the way of tangible results and have been widely criticized in legal, financial, and regulatory communities. Drawing on thirty‐five interviews with members of these units, as well as outside stakeholders including Crown Attorneys and private litigators, this article examines the activities of these Integrated Market Enforcement Teams and highlights a number of barriers to the successful execution of their designated mandate. While factors such as procedural restrictions and limited expertise are certainly relevant, this analysis reveals that the IMET teams are more fundamentally constrained by their position in a broader regulatory field. Understanding this field, and its unique structure and politics, is essential in coming to terms with both the possibilities and limitations of securities enforcement in an increasingly complex financial world.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.027 | 0.043 |
| Scholarly communication | 0.021 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".