Reimaging Ethics of Research with Refugees and Migrants: A Collaborative Autoethnography on the Ethics of Research
Bibliographic record
Abstract
Abstract This article employs a collaborative autoethnography (CAE) to examine how institutional research ethics frameworks shape the experiences of racialized graduate students conducting research with refugees and migrants. Grounded in intersectional feminist, the analysis interrogates how researchers’ positionalities intersect with institutional processes to shape access, participation, and knowledge production. From our analysis, two central themes emerge. First, Colonial Ethics of Care highlights how Western research ethics, oriented toward liability management and institutional protection, often silence marginalized voices and reproduce colonial logics of care. We argue for reframing ethics through relational accountability and community participation that foreground dignity, reciprocity, and justice. Second, The Ethics of Access: Resources and Support demonstrates that access extends beyond regulatory approval, encompassing financial constraints, institutional gatekeeping, and cultural misalignments that shift the burden of ethical responsibility onto individual researchers. Together, these themes underscore that ethical research with marginalized communities requires moving beyond procedural compliance toward practices of solidarity, care, and more resources for researchers.
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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 | 0.039 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.046 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".