Judges Talking To Jurors in Criminal Cases: Why U.S. Judges Do It So Differently From Just About Everyone Else
Bibliographic record
Abstract
Canada, a ten-minute drive of a mere 3.28 kilometers.4 She is not at all concerned about going beyond the giving of jury instructions.In fact, if she does not, she is likely to be reversed on appeal, perhaps even disciplined.And, it is not just that judge in Windsor.A judge in Auckland, one in London, one in Sydney, each would feel no hesitation going beyond a statement of the law and would likely be obliged to do so.Why the difference between U.S. judges and judges from other common law based nations, with similar roots in the English criminal justice system?After sitting through trials in several different nations over the past few decades, that became a nagging question for me.Are Americans really that different from their English-speaking cousins on this point?5 What explains that difference?And which nation gets it right?Those are the questions I intend to answer in this article.To do so, I take an unconventional approach.Of course, I will briefly discuss the well-established legal principles one finds in cases, statutes, and rules in the five focal nations of Australia, Canada, England, New Zealand, and the United States.In my research, however, I sought to go beyond this, to find out the way in which the practice really occurs.In short, I was trying to determine whether the trial judges truly acted so very differently in the various nations.I was in touch with more than eighty individuals in these five nations.6 Most I knew; all were experienced in the world of criminal justice, as trial or appeals 4. 2.04 miles.5.This is not the only point involving criminal procedure where the common law nations differ.Sharp contrasts can be drawn regarding the role and accessibility of the jury in the criminal trial, rules of exclusion, protections against self-incrimination, double jeopardy, sentencing, and open proceedings.I have-with my friend and colleague Professor Vicki Waye-twice before addressed such points in looking at Australia and the United States.See generally Paul Marcus & Vicki Waye, Australia and the United States: Two Common Criminal Justice Systems Uncommonly at Odds, Part 2, 18 TUL.J. INT'L & COMP.L. 335 (2010); Paul Marcus & Vicki Waye, Australia and the United States: Two Common Criminal Justice Systems Uncommonly at Odds, 12 TUL.J. INT'L & COMP.L. 27 (2004).6. Five from New Zealand (Auckland, Christchurch, and Wellington).New Zealand has a population of roughly 4,327,944 people.CIA WORLD FACT BOOK, https://www.cia.gov/library/publications/the-world-factbook/rankorder/2119rank.html(estimates as of July 2012) (last visited Mar. 6, 2013).Nine from Canada (Alberta, British Columbia, Ontario, and Saskatchewan).Canada's population is 34,300,083 people.Id. Eighteen from Australia (South Australia, Victoria, Western Australia, Queensland, and New South Wales).Australia's population is 22,015,576 people.Id.Ten from England (Brighton, Exeter, London, Nottingham, and Sheffield).England has a population of 52,000,000 people.OFFICE OF NATIONAL STATISTICS, http://populationofengland.co.uk
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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 | 0.053 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.036 | 0.027 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 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".