Bibliographic record
Abstract
Financial crime in Canada remains a mystery: omnipresent, but we know little about its operation. Transactions are cloaked with apparent legality, which makes tracking criminal activity through economic or financial statistics a complex undertaking. This distinctive volume aims to stem in-, out-, and through-flows of vast sums of dirty money by enhancing Canada’s capacity to detect, disrupt, deter, investigate, and prosecute domestic financial criminals and transnational organized criminal organizations. It brings together leading scholars and practitioners from the public and private sectors to identify and explore deficiencies in federal and provincial policy, regulation, legislation, politics, institutions, and enforcement, as well as the international financial crime regime. Together contributors pinpoint weaknesses that have turned the Canadian federation into a destination of choice for global financial crime, where its perpetrators can operate with impunity. Dirty Money reveals how globalization and technology have spun an extensive web of clandestine processes that disguises how financial criminals operate, the channels they use, and how they suborn banks and institutions. In the process, the extent of financial crime in Canada and its corrosive effects on communities, democratic institutions, and prosperity becomes apparent. Contributors: Sanaa Ahmed, John Cassara, Garry Clement, Arthur J. Cockfield, Caroline Dugas, Jamie Ferrill, Cameron Field, Michelle Gallant, Peter German, Rhianna Hamilton, Todd Hataley, Caitlyn Jenkins, Christian Leuprecht, David Maimon, Katarzyna (Kasia) Mcnaughton, Denis Meunier, Pierre-Luc Pomerleau, Stephen Schneider, Jeffrey Simser.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.156 | 0.025 |
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".