‘I have a story to tell’: Researching migrant women’s experiences of female genital mutilation and gender-based violence in Ireland and Europe
Bibliographic record
Abstract
This article presents insights and practical lessons learned from multiple studies the author has undertaken and participated in as principal or co-researcher and/or provided expert guidance to in Ireland and Europe. These studies primarily focus on gender-based violence (GBV) and female genital mutilation (FGM) and given their foci, have an implicit need for cognisance of child protection, legislation and onward referral procedures. The research issues of interest are often considered taboo, private, not to be discussed outside immediate family and shameful. There are multiple practical and logistical barriers, as well as language and psycho-social obstacles, to participating in, and undertaking, research on these issues. The article discusses the approaches and routes taken to recruit women affected and impacted by the issues of FGM and GBV for research studies. The responsibility on researchers to present research study findings in a sensitive manner which does not add stigma to marginalised and vulnerable groups, but that enables policy makers to utilise the research for legislative and practical purposes, is also discussed.Keywords: gender-based violence (GBV); female genital mutilation (FGM); migration; ethics; stigma; research design
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 | 0.022 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".