Blinded by the White: A Comparative Analysis of Jury Challenges on Racial Grounds
Bibliographic record
Abstract
Indigenous peoples in Australia, the United States and Canada are significantly overrepresented as defendants in criminal trials and yet vastly underrepresented on juries in criminal trials. This means that all-white juries mostly determine the guilt of Indigenous defendants or white defendants responsible for harming Indigenous victims. In this article, we explore cases in which Indigenous defendants have perceived that an all-white jury’s prejudice against Indigenous people would prevent them receiving a fair trial. It focuses on Indigenous defendants (often facing charges in relation to protesting against white racism) challenging the array of all-white juries. Across these cases, Australian courts rely on formal notions of fairness in jury selection to dismiss the Indigenous defendant’s perception of bias and foreclose an inquiry into the potential prejudices of white jurors. We compare the Australian judicial ‘colour-blindness’ towards all-white juries with that of the United States and Canada. We argue that the tendency for courts in the United States and Canada to question jurors on their biases provides useful lessons for Australian judiciaries, including in relation to the impending trials of Indigenous defendants in Kalgoorlie, Western Australia, accused of committing crimes in response to white racist violence. Nonetheless, across all jurisdictions where there is a challenge to the array based on racial composition, courts consistently uphold all-white juries. We suggest that the judicial view of the racial neutrality of white jury selection misapprehends the substantive biases in jury selection and the injustice perceived by defendants in having a white jury adjudicate an alleged crime that is committed in circumstances involving protest against white prejudice.
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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 | 0.034 | 0.136 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 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".