Do Juries Understand the Criminal Standard of Proof of Beyond Reasonable Doubt?
Bibliographic record
Abstract
In Green v The Queen and La Fontaine v The Queen, the High Court stated that it is both unnecessary and unwise for a trial judge to seek to explain to the jury the meaning of ‘beyond reasonable doubt’, on the ground that the phrase is well understood in the community. This view is not shared in other countries with a common law tradition such as England & Wales, Canada, New Zealand and the United States. This article respectfully disagrees with the High Court’s position, most recently affirmed in The Queen v Dookheea, and argues that Victoria has taken the appropriate course in enacting sections 63-64 of the Jury Directions Act 2015 (Vic). Section 63 sets out the circumstances when the trial judge may explain ‘proof beyond reasonable doubt’, and s 64 deals with the situation as to how the explanation may be given in response to jury question. However, it is contended that both s 63 and s 64 can be improved by removing the requirement that an explanation of the phrase ‘proof beyond reasonable doubt’ may only occur in response to a direct or indirect jury question. The argument will be developed in the context of a number of sexual assault cases where the guilty verdicts have been overturned on appeal, such as Tyrell v The Queen, IW v The Queen, JN v The Queen, Xu v The Queen, and Pell v The Queen.
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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 | 0.032 | 0.187 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.018 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".