An exploration into domestic abuse and so-called 'honour'-based violence and abuse experienced by South Asian women
Bibliographic record
Abstract
This thesis sets out to contribute to the gap in literature exploring how harm against South Asian women, through the perpetration of domestic abuse and so-called ‘honour’-based violence and abuse, can be prevented. Chapter One provides an overview of domestic abuse and so-called ‘honour’-based violence and abuse within South Asian communities and the vulnerabilities of women belonging to this population. \n \nChapter Two, a systematic review of 15 papers on so-called ‘honour’ killings in South Asia and the Middle East, finds differences in characteristics of victims and perpetrators of so-called ‘honour’ killings in India and Pakistan. A link between religion and so-called ‘honour’ killings is not found; however, some socio-demographic factors may contribute to supportive attitudes towards these murders. The prevailing factor maintaining so-called ‘honour’ killings appears to be patriarchy. Chapter Three critically evaluates the Ontario Domestic Assault Risk Assessment used to assess domestic abuse recidivism risk. It finds support for the risk assessment’s predictive ability; however further research is required to assess its reliability and use among different ethnicities. Chapter Four, an empirical study examines factors that may encourage female South Asian survivors (n = 80) of domestic abuse and honour- based violence and abuse to seek support sooner from a charity based in the UK. The trends from the study indicates that acculturation, frequency of abuse and support in keeping children safe, may be associated with quicker formal support-seeking, but further research is required. \n \nChapter Five discusses the thesis highlighting theoretical and practical implications and future direction for research on how harm experienced by South Asian women can be reduced.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".