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Reimaging Ethics of Research with Refugees and Migrants: A Collaborative Autoethnography on the Ethics of Research

2025· article· pt· W4417120758 on OpenAlexaff
Meray Sadek, Neela Hassan

Bibliographic record

VenueREMHU Revista Interdisciplinar da Mobilidade Humana · 2025
Typearticle
Languagept
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAutoethnographyCognitive reframingRefugeeSilenceInformation ethicsResearch ethicsEthnographyAccountability

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.204
metaresearch head score (Gemma)0.032
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesMetaresearch, Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.2040.032
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.013
Science and technology studies0.0080.036
Scholarly communication0.0010.000
Open science0.0030.003
Research integrity0.0010.019
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.378
GPT teacher head0.607
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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