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Record W3048937098 · doi:10.1177/1363460720944589

Doing, being and verbalizing: Narratives of queer migrants from Muslim backgrounds in Spain

2020· article· en· W3048937098 on OpenAlexfundno aff
Gerard Coll‐Planas, Gloria García-Romeral, Blai Martí Plademunt

Bibliographic record

VenueSexualities · 2020
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsnot available
FundersGeneralitat de CatalunyaEuropean CommissionUniversitat Oberta de CatalunyaFundación Española para la Ciencia y la TecnologíaUniversity of Victoria
KeywordsHuman sexualityGender studiesQueerNarrativeHegemonyCitizenshipSociologyIdentity (music)Sexual orientationSexual identityPoliticsAestheticsPolitical scienceLaw

Abstract

fetched live from OpenAlex

The hegemonic narrative in the West establishes that having same-sex relationships constitutes an identity that must be public. This article analyses how this narrative is reproduced and/or subverted in the discourses of queer migrant people from Muslim backgrounds in Catalonia (Spain). The analysis of 10 interviews reveals a more fluid notion of sexual orientation, an uncomfortableness with the identity categories regarding sexuality, and a stronger distinction between the public and the private boundaries. The informants found themselves in a complex situation that made it impossible for them to completely reproduce or subvert the overlapping normativities of both the origin and host society, compelling them to devise hybrid strategies to live their sexuality. The article closes with a reflection on the implications of the different ways of living sexuality in relation to the theorization of sexual/intimate citizenship and LGBT equality policies, which also reproduce the western hegemonic understanding of sexuality.

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)0.004
metaresearch head score (Gemma)0.004
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 score0.041
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0100.011
Scholarly communication0.0050.003
Open science0.0010.005
Research integrity0.0020.003
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 head0.057
GPT teacher head0.353
Teacher spread0.297 · 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

Citations10
Published2020
Admission routes1
Has abstractyes

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