Bibliographic record
Abstract
The Canadian criminal jury system has some unique characteristics. In contrast to American law, that gives precedent to free speech over fair trial, and English law, that favors fair trial over free speech, Canadian law occupies a middle ground balancing these competing values. Jury selection procedure in most trials is similar to that of England: jurors are assumed to be "impartial between the Queen and the accused" and are selected without voir dire. However, in cases involving exceptional pretrial publicity or involving accused persons from racial or ethnic minority groups, jurors are vetted by a "challenge for cause" process in which members of the jury pool itself, selected as "triers," determine which prospective jurors are impartial. Another totally unique aspect of the Canadian system is that special rules apply to juries in Canada's arctic regions. In addition to the official English and French languages, unilingual aboriginal persons who speak one of two Inuit dialects or one of seven Dene (Indian) dialects are eligible to serve on the jury. The purpose of this language provision is to provide for cultural perspective in jury verdicts and to increase community acceptance of Canadian law.
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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.017 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".