Power Dynamics in Police Interviews: A Comparison Between Witness and Suspect Interviews in Canada
Bibliographic record
Abstract
Interviewing is a critical component in police investigation and the judicial process. As a type of institutional discourse, police interviews are asymmetrical talks, in which participants are expected to speak and act within their own institutional and discursive role (Thornborrow 2002). In general, the interviewers have more power to influence and control the interaction through different discursive practices (Fairclough 1989; Thornborrow 2002), such as the selection of topics, the choice of question types, including questioning sequences, and the overall control of the duration of talk by other participants. However, it is not uncommon to see resistance from the interviewees too. There are typically two types of police interviews: interviews of suspects and interviews of victims or witnesses. Despite the different goals and purposes, studies have shown that suspect and witness interviews in Canada share similar features, such as the dominance of the interviewers, reliance on closed-ended questions and frequent interruptions from the interviewers to name a few. These practices raise concerns as they can contaminate interviewees' memory and may lead to false confessions (Snook et al. 2012; King & Snook 2009; Wright & Alison 2004).\n\nWith limited studies focusing on victim or witness interviews, the resistance strategies of interviewees and, more generally, police interviews in Canada, this major research paper explores power constructions and resistance in interviews of suspects and witnesses in Canada through a high-profile murder case. Section 2 discusses some relevant literature related to the theoretical frameworks and police interviews in Canada and defines some key concepts. Section 3 introduces the data and the data collection, transcription and coding process. Section 4 focuses on the analysis of individual interviews, with excerpts from the data. Section 5 compares the discursive features across interviews. Finally, Section 6 summarizes the findings and briefly discusses the limitations and future research ideas.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.030 | 0.010 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".