Bibliographic record
Abstract
Intimate partner violence (IPV), defined as physical, sexual, emotional, and economic abuse and controlling behaviors inflicted within intimate partner relationships, is a global crisis that extends beyond national and sociocultural boundaries, affecting people of all ages, religions, ethnicities, and economic backgrounds. Though studies exist that seek to explain how people become trapped within violent relationships and what factors facilitate survival, escape and safety, this book provides fresh insights into this complex and multifaceted issue. People often ask of women in abusive relationships “why does she stay?” Critics suggest that this question carries implicit notions of victim blame and fails to hold to account the perpetrators of abuse. The studies described in this book, however, explore the question from the perspectives of survivors and represent a shift away from individual pathology to an approach based on the recognition of structural oppression, agency and resilience. Comprising eight chapters, new theoretical frameworks for the analysis of IPV are provided to guide practitioners and policy makers in improving services for vulnerable people in abusive relationships, and a range of studies into the experiences of a diverse range of survivors, including mothers in Portugal, women who experienced child marriage in Uganda, and refugees in the United States of America, generate findings which elucidate perspectives from marginalised and under-researched groups.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.011 |
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".