Gender-based and intersectional violence in migration and refugee contexts: A contextual global approach
Bibliographic record
Abstract
Gender-based violence is a major infringement of women’s human rights, and an obstacle to sustainable development as set out in the Sustainable Development Goals. In this article, we explore both the processes and findings of our international comparative project on gender-based violence in migration contexts. Our research takes a feminist, intersectional, collaborative, and contextual approach to understand gender-based violence in the context of migration, analysing the ways in which discriminations and inequalities based on gender, race, nationality, ethnicity, sexual orientation, gender identity and age, interact to make certain women more vulnerable to gender-based violence. While we start from the lived experiences of women and persons working with them, we engage meso- and macro-level analyses of border practices, reception conditions and policy implementation, policies and legal systems that exacerbate their plight in order to understand the underlying dynamics that re(produce) patterns of violence. Our project shows the need for situated and contextual analyses of gender-based violence in national contents, but also the global rootedness of gender-based violence, and the key role of States in creating the structural conditions for the production of gender-based violence in migration contexts.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.009 | 0.032 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| 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".