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Record W1834597374 · doi:10.25071/1920-7336.38602

Queer Settlers: Questioning Settler Colonialism in LGBT Asylum Processes in Canada

2014· article· en· W1834597374 on OpenAlexaffvenueabout
Katherine Fobear

Bibliographic record

VenueRefuge Canada s Journal on Refuge · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRefugeeIndigenousSettlement (finance)ColonialismForced migrationGender studiesQueerPolitical scienceCriminologyState (computer science)Identity (music)SociologyLaw

Abstract

fetched live from OpenAlex

Refugee and forced migration studies have focused primarily on the refugees’ countries of origin and the causes for migration. Yet it is also important to also critically investi- gate the processes, discourses, and structures of settlement in the places they migrate to. This has particular signifi- cance in settler states like Canada in which research on refugee and forced migration largely ignores the presence of Indigenous peoples, the history of colonization that has made settlement possible, and ways the nation has shaped its borders through inflicting control and violence on Indigenous persons. What does it mean, then, to file a refugee claim in a state like Canada in which there is ongoing colonial violence against First Nations communities? In this article, we will explore what it means to make a refugee claim based on sexual orientation and gender identity in a settler-state like Canada. For sexual and gender minority refugees in Canada, interconnected structures of col- onial discourse and regulation come into force through the Canadian asylum and resettlement process. It is through this exploration that ideas surrounding migration, asylum, and settlement become unsettled.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.150
Threshold uncertainty score0.986

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.005
Science and technology studies0.0550.027
Scholarly communication0.0110.003
Open science0.0030.009
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0050.000

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 head0.009
GPT teacher head0.255
Teacher spread0.246 · 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 source (direct Gemma or distilled Codex), 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

Citations26
Published2014
Admission routes3
Has abstractyes

Explore more

Same venueRefuge Canada s Journal on RefugeSame topicMigration, Refugees, and IntegrationFrench-language works237,207