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Record W4408981270 · doi:10.29333/ejecs/2212

Higher Education’s Care/Control of Refugee and Displaced Students

2025· article· en· W4408981270 on OpenAlexafffund
Lisa Ruth Brunner, Takhmina Shokirova, Mostafa Gamal, Sharon Stein

Bibliographic record

VenueJournal of Ethnic and Cultural Studies · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and experiences of immigrants and refugees
Canadian institutionsUniversity of British Columbia
FundersCanadian Bureau for International Education
KeywordsRefugeeControl (management)Political sciencePsychologyMathematics educationComputer scienceLawArtificial intelligence

Abstract

fetched live from OpenAlex

There is growing interest in higher education’s intersections with displacement, a term used here to encompass the movement of refugees, asylum seekers, and those from otherwise forced or precarious international migration backgrounds. In particular, higher education institutions’ infrastructure and student support services are sometimes leveraged in response to displacement crises. Here, we propose a conceptual distinction between higher education’s reception and recruitment of displaced students, which share similar characteristics yet function in structurally different ways. We then consider how the modern/colonial global imaginary informs higher education’s relationship to bordering regimes and the framing of displaced students. We suggest that in addition to being problematically positioned as ‘charity’ - and, to a lesser extent, ‘cash,’ ‘competition,’ and ‘labor’ - some displaced students are also produced as ‘threats’ by bordering regimes. This highlights the importance of recognizing the ‘care/control nexus’ – that is, how care simultaneously operates as a form of control in the context of humanitarianism. We suggest the concept of ‘implicated subjects’ can help those embedded in higher education institutions move beyond overly simplistic victim/perpetrator/bystander categorizations in relation to supporting displaced students. We also offer one social cartography and two sets of hyper-self-reflexive questions as pedagogical tools to examine the imprint of a colonial system on both our higher education institutions and those of us who work within them. We suggest adopting an ongoing practice of hyper-self-reflexivity in order to respond differently to the impacts of current displacement crises and better prepare us for those to come.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score0.219

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.032
GPT teacher head0.441
Teacher spread0.409 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations7
Published2025
Admission routes2
Has abstractyes

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