Social Integration and Domestic Violence Support in an Indigenous Community: Women’s Recommendations of Formal Versus Informal Sources of Support
Bibliographic record
Abstract
Throughout North America, indigenous women experience higher rates of intimate partner violence and sexual violence than any other ethnic group, and so it is of particular importance to understand sources of support for Native American women. In this article, we use social network analysis to study the relationship between social integration and women's access to domestic violence support by examining the recommendations they would give to another woman in need. We ask two main questions: First, are less integrated women more likely to make no recommendation at all when compared with more socially integrated women? Second, are less integrated women more likely than more integrated women to nominate a formal source of support rather than an informal one? We use network data collected from interviews with 158 Canadian women residing in an indigenous community to measure their access to support. We find that, in general, less integrated women are less likely to make a recommendation than more integrated women. However, when they do make a recommendation, less integrated women are more likely to recommend a formal source of support than women who are more integrated. These results add to our understanding of how access to two types of domestic violence support is embedded in the larger set of social relations of an indigenous community.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".