AN INTERSECTIONAL ANALYSIS OF RESPONSES TO INTIMATE PARTNER VIOLENCE IN TWO MARGINALISED SOUTH AFRICAN COMMUNITIES
Bibliographic record
Abstract
This paper aims to investigate the responses available to urban and rural community members in the Western Cape Province of South Africa after witnessing, experiencing, or hearing about intimate partner violence (IPV) against women. It explores the social and material spaces that make IPV against women possible in these communities, which have a complex history of multiple forms of violence, including institutional, symbolic, and interpersonal. Seven focus group discussions with community members are analysed, using thematic narrative analysis, to explore the social and collective features of IPV and how it emerges within community responses to this violence. Constructions of IPV as an “everyday” event surfaced in the data, and mutualising language was often employed to construct IPV as a reciprocal activity with no clear distinction between attacker and victim. Also, a reconciliatory “kiss-and-make-up” narrative emerged in the data, representing how community members responded to this violence. In addition, the temporary nature of the violent event was emphasised by participants, and the aftermath was described as an opportunity for the victim and perpetrator to “reunite”, thereby providing justification for non-intervention in future violent events. By asking questions about responses to IPV, this paper offers insight into, and recommendations about, key forms of community intervention and engagement for gendered violence.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".