What is Going on Here? Police Framing, Misframing and Reframing of Criminal Responsibility in Intimate Partner Violence Cases
Bibliographic record
Abstract
Police officers responding to intimate partner violence (IPV) calls face numerous challenges, including distinguishing IPV from stranger violence, addressing conflicting information, and, notably, utilizing their discretion. This research aimed to scrutinize the decision-making processes employed by police officers during IPV calls for service. To achieve this, Goffman’s (1974) frame theory was applied to analyze semi-structured interviews conducted with one Winnipeg Police officer and three Winnipeg service providers. The objective was to observe participants’ conceptual maps that highlight the use of discretion, implicit biases, and the cognitive processes underpinning police decision-making. Participants were asked open-ended questions regarding their experiences with working alongside the police or as an officer responding to IPV calls for service. The findings show that, like other professionals, the officer in the study relied on past experiences and personal identity (such as being a woman or a mother) in their decision-making. While the officer justified their actions using professional guidelines (like arrest mandates and reasonable grounds), there were inconsistencies in the application of the guidelines alongside personal biases. To better understand police behaviour in responding to intimate partner violence (IPV), it is helpful to adopt a sociological lens. This approach examines how social structures, cultural norms, and power dynamics influence officers’ actions and the factors beyond their control that affect their decisions and outcomes. It is important to note that the sample used in this study cannot be generalized, but as an exploratory study, it contributes to the ongoing discourse about what is necessary to enhance police response to IPV.
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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 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.017 | 0.028 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| 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 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".