East Asian Women’s Domestic Violence and Help-Seeking Experiences at the Intersection of Gender, Ethnic, and Migratory Disadvantages in Canada
Bibliographic record
Abstract
This paper is motivated by the harm of domestic violence to East Asian women. Considering the multiple identities of this group, intersectionality is adopted as the theoretical framework. Based on an extensive literature review, the first part of the paper articulates three particular challenges associated with East Asian women’s ethnic, migratory, and gender identities, including myths of the model minority and “perpetual foreigners,” racialized sexism/sexualized racism, and Confucian patriarchy.\nThe second part explores how those unique challenges intersect to constitute disadvantages for East Asian women, rendering them vulnerable to domestic violence. First, unemployment and underemployment due to unrecognized foreign credentials, and discrimination based on the assumed problematic communication styles, leading to their financial insecurity. This makes it difficult for these women to leave their violent partners. Another barrier is to use the practices of the home country to deal with the current experience. This is because, on the one hand, the police and judiciary in where they come from may not regard domestic violence as serious, creating their mindset that it is useless to call the police; on the other hand, out of the concern for collective honor, the ethnic community may persuade them to make compromises, and those who refuse to cooperate may even be stigmatized. This emotional and social pressure makes East Asian women hesitant to leave their abusive partners in many cases. Finally, the paper ends with a summary of all the arguments, and also provides a prospect for future scholarship on related topics.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.021 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".