The terrorist resourcing model applied to Canada
Bibliographic record
Abstract
Purpose This paper aims to examine whether the money laundering/terrorist financing (ML/TF) model excludes important aspects of terrorist resourcing and whether the terrorist resourcing model (TRM) provides a more comprehensive framework for analysis. Design/methodology/approach Research consisted of case studies of resourcing activities of four listed terrorist organizations between 2001 and 2015: the Liberation Tigers of Tamil Eelam (LTTE), Hamas, a grouping of Al Qaeda-inspired individuals and entities under the heading “Al Qaeda inspired” and Hezbollah. Findings The most prevalent resourcing actors observed were non-profit organizations/associations, and the most prevalent form of resourcing was fundraising that targeted individual cash donations of small amounts. Funds were pooled, often passed through layers of charitable organizations and transmitted through chartered banks. The TRM is indeed found to provide a more comprehensive framework for identifying sources of resourcing and points of intervention. However, it does not in itself recommend effective means of response but it has implications for counter-resourcing strategies because it identifies resourcing actors and nodes where counter-resourcing could occur. Originality/value This paper advances the state of knowledge of terrorist resourcing activities in Canada and about the value of doing so through the analytical lens of the TRM as opposed to the predominant ML/TF model.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".