Status Compatibility and Help-Seeking Behaviors Among Female Intimate Partner Violence Victims
Bibliographic record
Abstract
Given the far-reaching social, personal, and economic costs of crime and violence, as well as the lasting health effects, understanding how women respond to domestic violence and the types of help sought are critical in addressing intimate partner violence. We use a nationally representative dataset (Canadian General Social Survey, Personal Risk, 1999) to examine the help-seeking behaviors of female intimate partner violence victims (N = 250). Although victims of violent crime often do not call the police, many victims, particularly women who have been battered by their partner rely on family, friends, social service, and mental health interventions in dealing with the consequences of violent crime. We examine the role of income, education, and employment status in shaping women's decisions to seek help, and we treat these economic variables as symbolic and relative statuses as compared to male partners. Although family violence researchers have conceptualized the association between economic variables and the dynamics of intimate partner violence with respect to the structural dimensions of sociodemographic factors, feminist researchers connect economic power to family dynamics. Drawing on these literatures, we tap the power in marital and cohabiting relationships, rather than treating these variables as simply socioeconomic resources. Controlling for other relevant variables we estimate a series of multivariate models to examine the relationship between status compatibilities and help-seeking from both formal and informal sources. We find that status incompatibilities between partners that favor women increase the likelihood of seeking support in dealing with the impact of violence.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".