The Problem of 'Relevance': Intelligence to Evidence Lessons from UK Terrorism Prosecutions
Bibliographic record
Abstract
As of November 2017, 60 known foreign terrorist fighters have been permitted to return and live in Canada without criminal consequence. The reason for this, according to the Minister of Public Safety, is the problem of using information collected for intelligence purposes as evidence in criminal proceedings. Often referred to as the “intelligence to evidence” (I2E) dilemma, this challenge has plagued Canada’s terrorism prosecutions since the Air India bombing in 1985. Yet, not all countries struggle to bring terrorist to justice. Canada’s prosecution statistics pale in comparison to the United Kingdom. In a democracy committed to upholding the rule of law and respecting human rights, prosecuting terrorists is the strongest and most transparent deterrent to this threat. This paper argues that as the threat of terrorism grows both domestically and abroad, Canada must learn from the UK’s experience and reform the rules of evidence to ensure that criminal charges are pursued. This paper will outline and compare the relevant Canadian and UK rules of evidence and assess their practical implications for national security prosecutions in light of primary research conducted in London in the fall of 2017. It concludes with a series of legislative and organizational reforms to improve the efficiency of Canadian terrorism trials.
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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 | 0.078 | 0.392 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.020 | 0.033 |
| Scholarly communication | 0.028 | 0.013 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 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".