POLICE UNDERSTANDINGS OF AND RESPONSES TO A COMPLEX VIGNETTE OF “HONOUR”-BASED CRIME AND FORCED MARRIAGE
Bibliographic record
Abstract
Police understandings of honour-based crimes (HBCs) and forced marriages (FMs) vary in terms of an individual officer’s level of expertise, knowledge, and experience in handling such situations. This study applied constructivist grounded theory approaches to analyze individual interviews with 32 police officers and 14 civilians in police agencies operating in urban and rural settings in Alberta, Canada. Specifically, this paper seeks to answer how police officers and civilians who work in police agencies experience, make sense of, and understand HBCs. Participants received a hypothetical vignette about a young woman who had reached out to the police. The vignette illustrated various forms of abuse by the woman’s father, the involvement of other actors (mother, brother, family friend) and the culmination in an FM. After reading the vignette, participants were asked to respond to six questions. Analysis revealed that both police and civilians recognized the need in the vignette scenario for intervention, while experiencing uncertainty about how to respond. The findings showed that not everyone in policing would be able to identify reliably the need for police intervention, and that investigations could proceed differently depending on the investigator’s level of knowledge and awareness of HBCs and FMs. Police have achieved some successful interventions, but still lack sufficient guidance on how torespond to these crimes. Clear, appropriate policies regarding which cases need to be directed to specialized domestic violence units for follow-up are needed. A significant finding points to the importance of considering cultural sensitivity discourses as well as the impact of cultural and racist stereotypes when responding to situations like the one outlined in the vignette.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".