The Consequences of Compelled Self-Incrimination in Terrorism Investigations: A Comparison of American Grand Juries and Canadian Investigative Hearings
Bibliographic record
Abstract
This paper compares American grand juries and Canadian investigative hearings as devices to compel persons to provide information for terrorism investigations in light of comparable protections against self-incrimination in both countries. The main protections of the constitutional right against self-incrimination may be too parochial in an era of global terrorism and the assertion of universal jurisdiction to prosecute terrorism. The main distorting effects are 1) attempts to delay the compelled testimony of a person detained as a material witness and to use compelled self-incrimination to engage in preventive detention, 2) attempts to use the fruits of compelled incrimination in jurisdictions that do not have to respect use and derivative use immunity, and 3) the use of contempt or perjury charges arising from the attempt to compel testimony rather than trials on the merits of the terrorism investigation. The distorting effects may be the price that a society has to pay for respecting constitutional protections against self-incrimination. The paper examines how Canadian investigative hearings offer more protections for compelled persons than American grand juries particularly in light of the Supreme Court of Canada’s extension of use and derivative use immunity for compelled testimony to extradition and deportation proceedings.
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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 | 0.010 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".