The Potential of Merging Intersectionality and Critical Ethnography for Advancing Refugee Women's Health Research
Bibliographic record
Abstract
Critical ethnography and intersectionality are increasingly engaged in nursing and refugee research. Both approaches study marginalized populations and explore how their daily experiences of inequality and marginalization are influenced by various forms of oppression, power structures, and cultural context. A blended approach of critical ethnography with intersectionality can inform research with marginalized groups as both have much in common, including the call for social justice and change. This article outlines the potential of using the blended theoretical approach in advancing refugee women's health research and to inform a particular methodological approach for nursing research and health care practice.
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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 | 0.161 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.020 | 0.009 |
| Science and technology studies | 0.011 | 0.052 |
| Scholarly communication | 0.025 | 0.038 |
| Open science | 0.004 | 0.055 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".