Seeking safety from male partner violence in Turkey: Toward a context-informed perspective on women's decisions and actions
Bibliographic record
Abstract
Women's stay/leave decision-making in violent relationships has become a subject of investigation in psychology over the last few decades. Despite making significant contributions to the understanding of how women's psychological processes shape their responses to violence, much of this research has lacked a contextualized approach. The present study aimed to provide a feminist context-informed examination of women's decision-making and safety-seeking processes. Twelve women who had experiences of violence in their marital relationships were interviewed individually. The study was carried out in Istanbul, Turkey, and all participants were socioeconomically disadvantaged women. A constructivist grounded theory approach was used for the data analysis. The results indicated that women's helplessness, beyond being a psychological construct, was a reality shaped by the conditions of marginalization in their lives. More than being related to the experience of psychological trauma, the women's narratives revealed the disempowering barriers associated with the lack of socioeconomic and institutional resources. Under these circumstances, regardless of their decisions to stay or leave, the women underlined their ongoing strategic efforts to ensure their safety, as mainly strengthened by the relational support available to them.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".