Dynamics of violence between intimate partners in the narratives of incarcerated women in Canada: A violent events perspective
Bibliographic record
Abstract
While we have learnt much in the last forty years about the prevalence of and risk factors for violence perpetrated by individual men and women in their intimate relationships, little research has focused on the interaction between intimate partners within a violent situation. I argue that given the complex nature of intimate relationships and intertwining roles, behaviours, and emotions of both partners, examining the couple's interaction -- rather than the disconnected behaviours of individual men or women -- can provide a deeper understanding of and new insights into the process of violence between intimate partners. Using a sample of 295 violent incidents reported by 135 incarcerated women, I explore the interactional aspects of violence in the incidents from a violent events perspective. Identifying and drawing on different dimensions of violent dynamics, e.g., initiation of violence, reaction to initial violence, the use of violence in the entire incident, and injuries to partners, I used Latent Class Analysis to identify specific classes of violent incidents that represent more general patterns of violent dynamics. This incident-based analysis identified four distinct classes of violent incidents and characteristics associated with each of them at the individual, relationship and situational levels. My study finds evidence consistent with previously identified violent types and also detects a novel type of violent dynamics in the relationships of high-risk incarcerated women. Moreover, the context-based and interaction-based approach in my study reveals the heterogeneity of women's behaviour in a violent situation. These findings question a `one size fits all' approach to address such a complex and multidimensional phenomenon as intimate partner violence and suggest carefully tailored interventions for different violent types/incidents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".