Uncovering the Presumption of Factual Innocence in\nCanadian Law
Bibliographic record
Abstract
The presumption of innocence has long been regarded as a hallmark of our justice system. Rhetoric abounds and finding a more celebrated legal doctrine is difficult. For most in the legalprofession, the presumption of innocence represents the procedural requirement that the Crown prove all elements of an offence. Yet, aside from its procedural and evidentiary protections, does the presumption of innocence offer any protection at the pre-charge phase of the criminal justice process? Specifically, for the majority of Canadians who have never been, or never will be charged with an offence, does the presumption of innocence offer any protection? Regrettably, Canadian law fails to explain how the presumption of innocence animates the pre-charge phase of the criminaljustice system, but rather contents itself merely to assert its relevance. The following paper offers a theoretical conception of the "pre-charge presumption of innocence". In an attempt to demonstrate its application and relevance, this theoretical model will be applied to an emerging technique of police investigation known as the DNA sweep.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.073 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| 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".