Cutting off the Flow of Funds to Terrorists: Whose Funds? Which Funds? Who Decides?
Bibliographic record
Abstract
One of the most important fronts in the newly declared war on terrorism is the financial one. The avowed aim of the United States and its allies is to cut off the flow of funds to terrorists. This campaign involves a two-pronged attack: The first prong involves prosecuting the financiers, i.e. individuals or organizations that provide money or property to support terrorist activities. The second prong involves freezing, seizing and forfeiting property that has been or might be directed toward terrorist activities. This paper analyses the portions of the Anti-terrorism Act that represent the federal government of Canada's first sortie on the financial front of the war against terrorism. Although the government's two-pronged strategy is simple to describe, it is actually inherently difficult to implement through legislation. One reason is because legislation of this sort is designed to capture economic activity that only poses a risk of contributing to future terrorist activity. This forces lawmakers to decide how much risk must be posed by a given activity before it ought to be criminalized, recognizing that the lower the threshold they establish, the more likely it is that they will capture activity that would not, if events proceeded in due course, actually lead to harm. A second challenge associated with legislation of this sort is to determine how close the connection between economic activity and terrorist activity must be in order for the economic activity to warrant criminal sanction. At some point the connection may be so remote that many reasonable people would conclude—for example, on the basis of concerns about personal liberty—that the economic activity should not attract criminal liability. My primary objective in this paper is a relatively modest one: I simply intend to describe how the drafters of the Anti-Terrorism Act have responded to the challenge of defining the relationship that must exist between individuals and property on the one hand, and terrorist activity on the other hand, in terms of both certainty and proximity, in order to trigger criminal penalties. Where appropriate I compare the approach taken in the new legislation to the approach that Canadian law has previously taken to similar issues, as well as to the approach adopted in the International Convention for the Suppression of the Financing of Terrorism (the 'Financing of Terrorism Convention'), which Canada signed on February 10, 2000. I do not attempt to assess directly whether the approach that the Anti-terrorism Act has taken is justifiable, since answering that question would involve canvassing a wide range of ethical, economic and political factors. However, towards the end of the paper, I do analyze the legislation in terms of the amount of power Parliament has given law enforcement officials, trial judges, juries and appellate courts respectively to determine which conduct should attract criminal sanction. I argue that some of the new provisions give law enforcement officials too much power and appellate courts too little.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".