Combating white‐collar crime in Canada: serving victim needs and market integrity
Bibliographic record
Abstract
Purpose This paper has two integrated purposes: it provides a report on a symposium hosted by the Bank of Canada and the Royal Canadian Mounted Police in December 2008 dealing with key challenges and directions forward for addressing white‐collar crime; and it ties this material into a conceptual review of the academic literature addressing the key conceptual, structural, legal, and cultural issues that impede the effective policing – broadly conceived – of white‐collar crime. Design/methodology/approach Participant‐observer in a symposium and literature review. Findings The original argument is put forward that the bedrock difficulties for dealing with white‐collar crime are conceptual: fundamental liberal capitalist beliefs about what markets are and how best they serve the well‐being of the population have resulted in a deep public‐private divide in law, institutional design, institutional culture, and institutional practice that often frustrates the types of collaboration and information sharing that are universally deemed essential for the effective policing of market space. Practical implications Coordinated experimentation across the enforcement spectrum must be undertaken, documented, and communicated with the purpose of identifying approaches that circumvent the known practical (i.e. legal, structural, and cultural) difficulties associated with the current political economy. Originality/value The value of this paper thereby lies in situating the practical obstacles to policing market space that face regulatory and enforcement actors, along with victims, in political economic context, so that alternatives that work beyond the limits of the current concepts become literally conceivable.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.029 | 0.011 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".