Bibliographic record
Abstract
The late Art Cockfield and I conceived this volume in our joint efforts to raise the profile of the insidious and pervasive nature of financial crime in Canada and transnationally.This project is part of a joint Insight Grant entitled Invisible Underworld: Inhibiting Global Financial Crime, funded by the Social Sciences and Humanities Research Council of Canada (grant number 435-2019-1333), and the Institute of Intergovernmental Relations at Queen's University.Art's expertise in tax law, evasion, and avoidance was unique, not just in Canada but globally.Among the most referenced tax law scholars in the world, his work was frequently cited by the Supreme Court of Canada.He became known to the general public as a distinguished subject expert in his field through numerous newspaper columns, featured articles, and a novel, The End.The Canadian government, the World Bank, and several other public and private sector stakeholders frequently sought him out to consult on such issues as combatting international tax evasion and other forms of cross-border financial crime.Particularly notable was his consultancy to the International Consortium of Investigative Journalists (ICIJ), initially in 2013 regarding tax haven data leaks that would explode into the Panama Papers.Among his seminal publications was the co-authored book International Taxation in Canada (LexisNexis 2006(LexisNexis , 4th ed.2018).Canada's Supreme Court, calling Cockfield a "learned author," would cite it thirteen times in Canada v. Alta Energy Luxembourg SARL, its 2021 decision so eagerly anticipated by the international tax community worldwide.When Art passed unexpectedly on 9 January 2022, we had already agreed on the contributors and Art had drafted his own contribution on tax evasion and aggressive tax avoidance, which appears posthumously in
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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 | 0.006 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.429 | 0.200 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".