Bibliographic record
Abstract
When Air India Flight 182 was bombed in 1985, anti–terrorist financing (ATF) law did not exist. In Canada, over the past decade, a broad-based legal regime, consisting of the Criminal Code and the Proceeds of Crime (Money Laundering) and Terrorist Financing Act, has developed. This regime covers significant regulatory ground and, generally speaking, accords with private and public international law, including the 1267 regime and the 1999 International Convention on the Suppression of Terrorism Financing. But has this regime been effective in fulfilling its stated objective of preventing the funding of terrorist activity? Unless we know the answer – and we do not – we should not be keen to impose additional legal requirements on private or public actors. Regulation is costly, and ineffective regulation imposes unnecessary costs on the private and public sectors. This issue of effectiveness is increasingly important, given the emphasis on terrorist financing in the international community as exemplified in the intergovernmental Financial Action Task Force and the United Nations Security Council. This article recommends a full-fledged assessment of the efficacy of the current ATF regime. Although the argument for undertaking an evaluation of ATF law was presented to the Commission of Inquiry into the Investigation of the Bombing of Air India Flight 182 by the author, the recommendations in the commission’s final report did not address terrorist financing or the need to assess the efficacy of the current legal regime, except for general recommendations about the role of the National Security Advisor. This is a conspicuous omission, given that ATF law has never been assessed or analysed in terms of its efficacy. The underlying assumption appears to be that the regime works well, but this assumption has not been subject to governmental scrutiny let alone empirical assessment.
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 | 0.014 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.020 | 0.015 |
| Scholarly communication | 0.023 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".