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Record W4306655064 · doi:10.1111/cag.12811

Trans migrations: Seeking refuge in “safe haven” Toronto

2022· article· en· W4306655064 on OpenAlexaffvenueabout
Tai Jacob, Natalie Oswin

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsThe Scarborough HospitalUniversity of TorontoYork University
Fundersnot available
KeywordsRefugeeCitizenshipImmigrationGender studiesTransgenderPolitical scienceSociologyQueerCriminologyLaw

Abstract

fetched live from OpenAlex

Trans and gender nonconforming (TGNC) people who make refugee claims in Canada negotiate a complex nexus of identity, belonging, and citizenship. Drawing on insights from TGNC refugees, immigration lawyers, and frontline workers, in this paper we examine the ways the state controls the trans body through the refugee claims process and in the process of integration into life in Canada, while also highlighting trans refugee methods of survival and resistance. What emerges is an understanding of the ways that refugees navigate the tension between gender, sexuality, and homecoming as both intimately felt and geopolitically managed. We convey TGNC refugee narratives to demonstrate how they both confirm and expand upon the existing literature on Canadian LGBTQ + refugees. TGNC refugees' experiences at the Immigration and Refugee Board confirm insights from existing LGBTQ + refugee studies. However, TGNC refugees' day‐to‐day lives differ significantly from LGB refugee lives as recounted in the literature. In TGNC refugees' attempts to access gender‐affirming documentation, healthcare, housing, and income, they confront distinct systems of transgender exceptionalism, border imperialism, and racial and heteropatriarchal capitalism that limit their access to basic necessities and impact how they build home both conceptually and materially .

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.806
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0040.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.231
Teacher spread0.221 · 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.

Study designNot applicable
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

Citations6
Published2022
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Geographies / Géographies canadiennesSame topicMigration, Refugees, and IntegrationFrench-language works237,207