Indigenous Canadians Take the Stand: The Influence of Age and Race on Mock-Juror Perceptions and Verdict Decisions
Bibliographic record
Abstract
Previous research has demonstrated that jurors’ perceptions of eyewitness and defendant credibility are often influenced by case-irrelevant factors such as race and age (Rogers & Davies, 2007; Pozzulo et al., 2010). Individuals of almost any age are eligible to testify as an eyewitness in trials, therefore, eyewitness age is relevant in juror decision-making. Overall, adult eyewitnesses are perceived with more integrity than child eyewitnesses in non-sexual assault cases (Bruer & Pozzulo, 2014; Sheahan et al., 2021). Regarding race, negative stereotypes surrounding Indigenous populations concerning criminality are prevalent within the Canadian criminal justice system, as can be seen with an overrepresentation of Indigenous offender populations (Cunneen, 2006). Systemic racism, racial biases, and adverse stereotypes have demonstrated impacts on mock-jurors’ perceptions of Indigenous defendants and eyewitnesses (Ewanation & Maeder, 2018). Although research has delineated how age and race impact juror decision-making in recent years (Pica et al., 2017; Maeder & Yamamoto, 2018), a gap exists in the literature for understanding how jurors perceive Indigenous eyewitnesses and defendants and whether these perceptions are influenced by eyewitness age. Specially, the current study aims to understand how eyewitness age and race will interact with defendant race to influence jurors’ perceptions of believability, credibility, and verdict decisions.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".