A Descriptive Analysis of How Canadian Police Officers Administer the Right-to-Silence and Right-to-Legal-Counsel Cautions
Bibliographic record
Abstract
The administration of the right-to-silence and right-to-legal-counsel cautions in 126 investigative interviews (37 videotapes, 89 transcripts) was evaluated with a 78-item coding manual. We found that the right-to-silence and right-to-legal-counsel cautions were administered in 87% and 83% of the interviews, respectively. Average speech rates for both cautions exceeded acceptable levels for ensuring listening comprehension. Although the right-to-silence and right-to-legal-counsel cautions were not always read verbatim, the interviewers rarely missed rights that are contained in the cautions or incorrectly read the cautions. Interviewees almost always confirmed that they understood both cautions, but interviewers rarely attempted to verify that they actually understood them. Attempts to explain various rights in both cautions were always done correctly. Interviewees invoked their right to silence in 25% of cases and chose to speak to a lawyer in 31% of cases. The implications of these findings for improving the administration of justice in Canada are discussed.
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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 | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".