Bibliographic record
Abstract
There is much to learn from the trial of Saskatchewan farmer Gerald Stanley on the dangers of not directly confronting the potential impact of racial bias on the trial process. Stanley was acquitted in February 2018 by an all-White jury in the shooting death of 22-year-old Cree man Colten Boushie. The law gives us tools to safeguard trials from racial bias that we shouldn’t ignore. One of these tools is the law of evidence. The law of evidence is a set of rules aimed at regulating the admissibility and use of evidence, in order to fairly promote the search for truth. It recognizes that judges and jurors bring to court every day assumptions about human experience and behaviour that are grounded in unreliable, stereotypical or discriminatory assumptions. That is precisely why it gives judges a discretion to exclude evidence where its prejudicial effect outweighs its relevance or probative value. And why we have rules, for example, that make prior sexual history evidence in sexual assault cases or evidence that paints an accused in a negative light (bad character evidence) presumptively inadmissible. Unfortunately, despite the fact that Indigenous, Black and Brown lived experiences are disproportionately before courts consisting of largely White jurors or judges, we have failed to ensure that our rules of evidence protect against racial bias in the same way that they do against other types of unreliable and discriminatory generalizations. The Stanley trial is a stark reminder of this reality. This short piece examines the Stanley trial and how the law of evidence can incorporate systemic racism as a lens to address issues of admissibility.
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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 | 0.296 | 0.614 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.016 | 0.028 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".