Intersectional Principles of Community Partnership and Social Justice in Qualitative Research in Migration
Bibliographic record
Abstract
This article examines the methodological implications of employing intersectional principles in qualitative health research conducted in migration contexts, specifically focusing on a doctoral research project on return migration and reunited couples in Mexico. The article highlights the integration of social justice and community-based research perspectives within an intersectional lens. Five key areas are examined, including recognizing diversity and agency among women who stay behind, navigating intersectional identities, understanding positionality, and advocating for populations made vulnerable by inadequate policies, navigating power dynamics and multiple social locations, and empowering the community through intersectional research. The application of intersectionality challenges homogenizing narratives, emphasizing the agency and resilience of women who stay behind. Reflexivity is crucial in mitigating biases and deepening insights, while collaboration with a local researcher enhances understanding of power dynamics. By empowering the community through advisory committees and culturally relevant dissemination, I aimed to amplify community member voices and promote social justice. This article serves as a valuable resource for researchers conducting intersectional qualitative health research in rural settings, offering guidance on integrating a strength-based approach, fostering intersectional reciprocity, and navigating positionalities.
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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 | 0.455 | 0.251 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.023 | 0.136 |
| Scholarly communication | 0.025 | 0.016 |
| Open science | 0.006 | 0.032 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".