Jury selection in Australia: Is there enough cause for challenge?
Bibliographic record
Abstract
This presentation will examine the jury empanelment process across Australian jurisdictions as well as outline a number of recent trends and issues that may suggest a need to open a dialogue regarding a refreshment of the selection phase in order to effectively (and procedurally) address identified juror biases. The issue of juror bias is one that has been long recognized by a variety of \nCommon Law countries including Canada and the United States and has recently been highlighted with regard to a number of high profile cases in Australia. A review of international Common Law approaches to jury selection indicates that the United States represents one of the more liberal approaches while Australia represents one of the more conservative approaches. Interestingly, although the Canadian system has adopted a more intermediate approach, the process is somewhat \ninformal and does not always align with the available scientific evidence. This presentation, therefore, will focus on: (1) outlining the available empirical evidence relating to potential juror bias in Australia, (2) identifying the major demographic and cognitive attributes that appear to impact juror bias, and \n(3) introducing a formalized, procedurally-based process for responding to this issue through the challenge for cause component of empanelment. These issues will be discussed within the Australian social and legal context.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".