Is this discursive Yentling? A critical study of an RCMP officer's interaction with a child sexual assault complainant
Bibliographic record
Abstract
The present study features an interview between a Royal Canadian Mounted Police (RCMP) officer and a female indigenous minor, who was reporting her own sexual assault. The study highlights how the child’s interview with the officer appears to include gender-specific judgements. Thus far, few critical studies, underscoring interview techniques, feature power relations and ideologies in the discourse. This study identifies police negotiation with female assault complainants as discursive Yentling. Inspired by the term Yentl syndrome , where female health is often underappreciated because it is judged from male prerogatives, the present study proposes that discursive Yentling emerges from victim blaming, perpetrator mitigation, and the sexualization of rape. Drawing attention to transcripts of an RCMP interview with a child complainant, this study asks (1) what power relations and ideologies manifest in the dialogue between the officer and the complainant? (2) Do the findings give evidence for discursive Yentling? Transitivity analysis and a discourse historical approach reveal ideological predispositions towards the complainant during the interview. The implications for this study hopefully provoke more considered police interview techniques for potential victims of sexual assault and inculcate a culture of feminist understanding in Canadian public services.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.029 | 0.031 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".