Towards a Social Justice Agenda: Intimate Partner Violence among Rural, African American Women
Bibliographic record
Abstract
The social work profession is rooted in community-based work that seeks to eradicate social injustice everywhere. Intimate partner violence (IPV) is a global phenomenon which impacts women from diverse socio-economic and racial/ethnic backgrounds. It involves power and control, economic abuse, and physical and sexual violence. When compared to other racial and ethnic groups, African American women are likelier to experience physical violence, rape, and homicide. Intimate partner violence among African American women is a social justice issue. When compared to other racial and ethnic groups, rural and/or low-income African American women are likelier to experience IPV. They are also likelier to experience psychosocial challenges and negative physical health outcomes due to the lack of availability, accessibility, and quality of IPV services. Individual, relationship, and community factors such as aggression, economic stress, and societal norms that uphold patriarchy contribute to IPV among rural, African American women. Social work practitioners, educators, practitioners, and researchers are uniquely qualified to use multi-level interventions to address the causes of IPV among rural, African American women. This work presents multi-level solutions to dismantle oppression and violence against rural, African American women. Such solutions would help improve economic, social, mental and physical health outcomes for rural and underserved communities largely impacted by 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 | 0.002 | 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 | 0.014 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".