Female Genital Cutting and African Women's Migration to Canada: Toward a Postcolonial Feminist Decolonizing Methodology
Bibliographic record
Abstract
The discourse on the cultural practice of FGC has captured a lot of attention over the last several decades, and with international migration propelling what was once a private cultural practice onto the global stage, FGC has become a fixture in the international human rights and global health arenas. FGC is a sensitive topic and the debates around it remain politically and culturally contentious. A lot of resources have been poured into eradication endeavors with multiple multinational organizations including the WHO spearheading the effort, the non-negotiable endpoint being the wellbeing, safety, and security of young girls and women. The purpose of this qualitative interpretive description study informed by decolonizing perspectives was to critically examine how immigrant and refugee women who have experienced FGC make sense of and explain the practice for themselves and for younger generations; and to explore the sociopolitical contexts sustaining and perpetuating FGC in the lives of affected younger and older women including their perceptions of as well as interactions with health services in diasporic locations. Participants’ understandings and agency-in-practice were analyzed through the themes of Experiencing, Explaining, Migrating, and Mitigating FGC. A decolonizing interpretation of research findings surfaced the intersections of social, political, economic, and cultural barriers manifesting through racialized and gendered axes of exclusion and marginalization to affect the health and wellbeing of FGC-affected immigrant and refugee women in a globalized milieu. This study highlights the importance of historical and cultural contexts in understanding and researching FGC-affected women as well as the relevance of decolonizing universal norms including in research, in order to effectively do this. This study offers an alternative way of conceptualizing FGC in a transnational setting and has implications for nursing research, health services, nursing education, as well as leadership and policy. Immigrant and refugee women affected by FGC deserve equitable, socially just, culturally responsive, and trauma-informed health services. This is in keeping with the nursing mandate of fostering health equity and social justice for all individuals. This study opens avenues for considering alternative ways of conceptualizing FGC and in doing so lives up to the interpretive description design logic.
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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 | 0.021 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.034 | 0.055 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".