Transnationalism and Hegemonic Masculinity: Experiences of Gender-Based Violence Among African Women Immigrants in Canada
Bibliographic record
Abstract
Gender-based violence (GBV) is an age-long issue plaguing societies all over the globe. Over the years, GBV perpetrated against women has been justified and legitimized by patriarchal and hegemonic masculine structures. This study explored the role of hegemonic masculinities and transnational cultural conflicts in creating a suitable environment for GBV against women newcomers from the continent of Africa. The study gathered perspectives of African immigrants and of the service providers working in immigrant-serving organizations. The paper adopts a qualitative approach and hinges on the transnationalism framework. This framework argues that immigrants maintain connections while transitioning to their destination countries. In such processes, immigrants carry with them their beliefs about cultural norms and hegemonic masculinity, of their country of origin. A total of 13 women immigrants and 20 service providers were purposively recruited to participate in the semi-structured interview. The interviews were recorded and transcribed verbatim. The data were analyzed thematically and organized using Nvivo version 12. Findings show that African immigrant women in Canada disproportionately bear the burden of GBV due to hegemonic masculinities. The construction of masculinity in immigrant populations is heavily reliant on the communities of origin. As such, the prevailing systems during and post migration such as—unstable residency status, fear of deportation, fear of social and family sanctions and stigmatization, economic dependence on their spouses, and fear of retaliation from their spouses creates an environment that supports toxic masculinity. The study recommends comprehensive and culturally sensitive programmes and services to support African immigrants affected by hegemonic masculinity and 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.034 | 0.010 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".