A content analysis of how police in Canada handle the right to silence
Bibliographic record
Abstract
Purpose In Canada, police are legally allowed to continue questioning suspects who invoke their right to silence, thereby encouraging the suspects to waive that right. This study aims to examine police responses to suspects’ right to silence invocations, focusing on how they encourage suspects to provide information and cooperate with the investigation. Design/methodology/approach This paper reviewed 24 archived video recordings (over 100 h) of Canadian police interviews, documenting suspects’ invocations of silence and officers’ responses. A directed content analysis was conducted to code and categorize the police officers’ responses to the suspects’ invocations. Themes were developed from these categories to capture the patterns in the officers’ responses. Findings Suspects invoked their right to silence often (M = 22.21 per interview; SD = 20.54) and police used a variety of strategies to handle the invocations. While most strategies aligned with findings from previous research, several newly identified strategies also emerged, with 33% of responses using more than one strategy. The most used strategies fell within domains found in previous research, such as confrontation and competition, emotion provocation and collaboration. Novel strategies included minimizing the role of legal counsel’s advice and using dismissive language to downplay silence. Originality/value To the best of the authors’ knowledge, this is the first study to identify the strategies Canadian police use to handle right-to-silence invocations, providing a foundation for future research examining how these strategies predict suspect cooperation in police interviews.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".