Hear My Cry: Breaking the Code of Silence around Intimate Partner Violence among Black Women in and beyond Midlife
Bibliographic record
Abstract
There is a knowledge gap about how the intersections of gender, race, culture, age, income, social class, and other identities shape Black women’s experiences of intimate partner violence (IPV). In this qualitative study, we utilized an intersectional approach to examine how IPV is experienced and managed by racialized women, and in particular, our focus was to explore the IPV experiences of Black Nova Scotian women in and beyond midlife and their experiences of seeking support. Participant recruitment was predictably challenging, but we were able to collect in-depth interview data from a Black woman who identified as being in and beyond midlife and who had experienced IPV in the past and from three people who provided support to Black women in a paid capacity. An interpretive narrative approach was utilized to identify five dominant themes: descriptions of the experiences of IPV for Black women; strategies for coping with IPV; strategies in supporting Black women experiencing IPV; barriers in accessing support; and challenges in the delivery of support. The knowledge gained through this research provides important insights about the experiences, barriers faced, and how to address these challenges for Black women who experience IPV in and beyond midlife.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.020 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".