South Asian Immigrant Men and Women and Conceptions of Partner Violence
Bibliographic record
Abstract
Limited knowledge exists about conceptual variations in defining intimate partner violence (IPV) by ethnicity, such as South Asian (SA) immigrant men and women. In a multi-ethnic study, we employed participatory concept mapping with three phases: brainstorming on what constitutes IPV; sorting of the brainstormed items; and interpretation of visual concept maps generated statistically. The parent study generated an overall general multi-ethnic map (GMEM) that included participant interpretations. In the current study, we generated a SA specific initial-map that was interpreted by eleven SA men and women in gender specific groups. Their interpretations are examined for similar and unique aspects across men and women and compared to GMEM. SA men and women shared similar views about sexual abuse and victim retaliation, which also aligned closely with GMEM. Both SA women and men had an expanded view of the concept of controlling behaviors compared to GMEM. SA women, unlike SA men, viewed some aggressive behaviors and acts as cultural with some GMEM congruence. SA women uniquely identified some IPV acts as private-public. We discuss implications for research and service assessments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".