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Record W2974573241 · doi:10.1177/2329496519875484

Refugees’ Transnational Practices: Gay Iranian Men Navigating Refugee Status and Cross-border Ties in Canada

2019· article· en· W2974573241 on OpenAlexaffabout
Aryan Karimi

Bibliographic record

VenueSocial Currents · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRefugeeGender studiesEmigrationSociologyPolitical scienceForced migrationImmigrationPopulationInterpersonal tiesQueerSocial scienceLawDemography

Abstract

fetched live from OpenAlex

Despite the rise in displaced population numbers, refugees’ transnational lives, and those of sexual-racial minority refugees in particular, have remained at the margins of transnational migration studies. In this article, I focus on the case of gay Iranian refugees in Canada and analyze their pre-migration transnational lives and understandings of the asylum process, their post-migration transnational ties, and their activism practices. I underline refugees’ transnational agencies and argue against the rhetoric that represents refugees as passive migrants whose emigration means detachment from home countries. Based on my field work findings, I endorse analytical and methodological shifts to simultaneously explore refugees’ pre-migration and en-route lives in addition to their post-migration lives to stress the power relations that, through social ties, affect refugees’ transnational practices. I connect transnational, forced, and queer migration literature to the Bourdieusian social theory and, in conclusion, argue that it is necessary to deploy de-nationalized methods of inquiry to account for intra-group diversities as well as border-crossing social ties in addition to economic ties.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.036
Threshold uncertainty score0.261

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0350.011
Scholarly communication0.0070.002
Open science0.0020.007
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.419
Teacher spread0.399 · 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

Citations22
Published2019
Admission routes2
Has abstractyes

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