The Role of Intimate Relationship Status, Sexuality, and Ethnicity in Doing Fieldwork among Sexual–Racial Minority Refugees: An Intersectional Methodology*
Bibliographic record
Abstract
Feminist researchers from a range of disciplines have called for consolidation of intersectionality as a methodology. In this article, I contribute to the literature on intracategorical intersectional methodology by drawing on my experiences of conducting fieldwork with 19 gay male Iranian refugees in Canada. Also, by merging the research on intersectionality, sexuality, and refugee studies, I take intersectionality beyond its traditional application on the lives of women of color. I particularly focus on relations between intimate relationship status and insider status, sexuality and internal gatekeepers, and ethnicity and obtaining signed consent forms. Assuming that ethnographers, albeit marginally, participate in or become part of their participant group during fieldwork, I demonstrated the utility of intracategorical intersectional methodology for a systematic examination of power dynamics and the interactions between participants’ and researchers’ markers of identity. I argue that intracategorical intersectionality challenges static definitions of insiderness in qualitative research and provides researchers with nuanced and non‐hegemonic analyses of research process.
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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 | 0.091 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.021 | 0.031 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.001 | 0.002 |
| 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".