WE WERE SAILING INTO UNCHARTED WATERS: FLAWS IN THE APPLICATION OF CANADA’S CRIMES AGAINST HUMANITY AND WAR CRIMES ACT
Bibliographic record
Abstract
In Canada’s two trials to date under the Crimes Against Humanity and War Crimes Act, serious flaws in the application of the Act have emerged, in particular regarding the framing of the indictment. In prior proceedings at the ICTR and ICTY, the indictments contained detailed recitations of the facts, including the specific “constitutive crimes” for which trials on the “chapeau crimes” of genocide, war crimes and crimes against humanity were held, and in which convictions and acquittals were based on these indicated “constitutive crimes”. In Canada, the indictments merely indicated the “chapeau crimes” and not the “constitutive crimes”, making it impossible for an accused to know precisely for what he is charged, negatively affecting trial preparation, and impossible to determine if a jury is actually unanimous on any given “constitutive crime”, effectively rendering illusory the right to a jury trial. The authors argue that the Canadian indictments foster a fundamental misunderstanding of the essential elements needed to prove the international crimes of genocide, war crimes and crimes against humanity, compromising the possibility of holding a fair trial under the Act.
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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 | 0.033 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.029 | 0.021 |
| Scholarly communication | 0.017 | 0.004 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.011 | 0.022 |
| 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".