The Unique Challenges of Terrorism Prosecutions: Towards a Workable Relation between Intelligence and Evidence
Bibliographic record
Abstract
The focus in this study isle on the unique challenges presented by terrorism prosecutions especially those relating to the disclosure of otherwise secret intelligence.. The first part of this paper will provide an historical outline of thinking about the distinction between security intelligence and evidence. The second part of this paper will outline some of the competing goals that should inform the relationship between security intelligence and evidence. The third part of this paper will examine issues related to the use of information collected by the Canadian Security Intelligence Service (CSIS) as evidence while intelligence is generally collected under less demanding standards than evidence. The fourth part of this paper will examine disclosure requirements as they may be applied to intelligence. The fifth part of this paper will examine possible legislative restrictions on disclosure and through the expansion or creation of evidentiary privileges that shield information from disclosure. The sixth part of this paper will examine existing means to secure nondisclosure orders to protect the secrecy of intelligence in particular prosecutions. The seventh part of this paper will examine the procedures used in the United States, the United Kingdom and Australia to resolve claims of national security confidentiality, with a view to understanding how the approaches used in those countries differ from those used in Canada and whether they provide a sounder basis for maintaining a workable and reliable relationship between security intelligence and evidence. The conclusion of this paper will assess strategies for making the relationship between intelligence and evidence workable. The strategies include both front-end strategies that will affect the collection of intelligence and back-end strategies relating to partial non-disclosure of intelligence that is not used as evidence. All of these issues are united by the need to establish a reliable, workable and fair relationship between intelligence and evidence. They raise fundamental questions about the viability of criminal prosecutions as a response to the threats of, and to acts of, international terrorism such as that which resulted in the bombing of Air India Flight 182.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".