The Intersection of Gender and Other Social Institutions in Constructing Gender-Based Violence in Guangzhou China
Bibliographic record
Abstract
Although violence against women is illegal in China, few studies have been published concerning this issue in that country. This article is part of a program of research undertaken in one province of China. The purpose of this study was to understand, from the perspectives of women who have experienced gender-based violence (GBV), the intersections of gender and other social institutions in constructing GBV in Guangzhou, China. The research question was as follows: For women who have been unfortunate enough to be with a partner who is willing to use abuse, how is gender revealed in their discussion of the experience? Women participants (N = 13) were all over the age of 21, had experienced some form of abuse in an intimate relationship, and had lived in Guangzhou at least for a year prior to data collection. They had a variety of backgrounds and experiences. The majority spoke of GBV as common. "Saving face" was connected to fear of being judged and socially stigmatized which had emotional as well as material consequences. Eight situations in which social stigma existed and caused women to lose face were identified. Gender role expectations and gendered institutions played a part in family relationships and the amount of support a woman could expect or would ask for. The women in this study received very little support from systems in their society. A high proportion (67%) revealed symptoms of mental strain, and three talked about having depression or being suicidal. The results are discussed in terms of identifying the mechanisms by which systems interlock and perpetuate GBV.
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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 | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".