"If we didn't know what happened, you wouldn't be sitting in that chair": Communicative Purposes and Rhetorical Actions in Canadian Police Suspect Interviews
Bibliographic record
Abstract
Miscarriages of justice carry devastating, lasting impacts for those who are wrongfully accused or convicted, as well as for their families, loved ones, and wider communities (Campbell & Denov, 2004; Schuller et al., 2021; Weigand, 2009). Research suggests that police interviews represent one of the earliest points from which a miscarriage of justice can originate (Campbell, 2018, p.85), having been described as the “most potent weapon” in securing a conviction (Kassin & Gudjonsson, 2004, p. 35). In the aim of better understanding the interactions of interviewers and suspects in police interviews, and in addressing the dearth of bilingual studies within this research sphere, this qualitative study examined 35 Canadian suspect interview transcripts in English (n=17) and French (n=18). A rhetorical move-step analysis (Swales, 1990) revealed a consistent rhetorical structure which facilitates three major communicative purposes: (1) gathering significant evidence, (2) creating a clear and consistent record, and (3) supporting the admissibility of interview evidence. Despite legal and cultural differences distinguishing English and French Canada (Department of Justice Canada, 2023; Gagnon, 2023; Turgeon et al. 2019), no significant rhetorical variation was observed across the English and French corpora examined in this study, suggesting that the texts’ communicative purpose (Swales, 1990) is the driving force governing the construction of suspect interviews by law enforcement. The findings of this study contribute to a growing body of work on Canadian suspect interviews (e.g., Eastwood, 2011; King & Snook, 2009; Snook et al., 2010a, 2010b, 2020) by providing an expanded understanding of interviewing practices in Canada and framing suspect interviews as a single, multipurpose genre (Swales, 1990). Moreover, this study expands on the work of previous research discussing the context (Bucholtz, 2009; Komter, 2012), purpose (Coulthard, 2002; Stokoe & Edwards, 2008), audience (Carter, 2011; Haworth, 2013) and participants (Haworth, 2009; Heydon, 2007; Momeni, 2011) of suspect interviews. As well, the conclusions of this work highlight aspects of suspect interviews that may present an increased risk for the miscarriage of justice. Overall, this study supports ongoing efforts to develop evidence-informed interviewing practices and provides recommendations to be carried forward in future work.
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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 | 0.016 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.073 | 0.030 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.010 |
| 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".