Public Duty versus Private Information: Jury Privacy in the Information Age
Bibliographic record
Abstract
The lay-jury remains a central feature of justice systems in many common law countries. Underpinning the nature of jury trials are two fundamental principles: representativeness and impartiality. In order to satisfy these principles, jurors will typically be asked to provide personal information. This disclosure presents the possibility that a juror’s private information may be misused. While such concerns have existed for some time, the advent of Information Communication Technologies has given them increased urgency. Surveys reveal that a significant number of jurors are concerned for their privacy and safety, presenting a conflict between the public duty of jury service and their personal right of privacy. This article considers the extent to which the state can and should protect the privacy of individuals called for jury service. Focusing on examples from Australia, Canada, the United Kingdom and the United States, it begins with a discussion of the extent to which jurors are required to disclose personal information. It then discusses various concerns that may arise as a result of that disclosure, particularly personal safety and public embarrassment. Finally, suggestions for reform are provided in an attempt to address these concerns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.067 | 0.138 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.016 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".