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Record W2405448952 · doi:10.1177/1354067x14551296

Iranian homosexuals; social identity formation and question of femininity

2016· article· en· W2405448952 on OpenAlexaff
Aryan Karimi

Bibliographic record

VenueCulture & Psychology · 2016
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFemininityMasculinityGender studiesHomosexualityIdentity (music)SociologySocial identity theoryHuman sexualitySexual identityFriendshipSocial psychologyPsychologySocial group

Abstract

fetched live from OpenAlex

Homosexuality and homosexual subjects have been oppressed during the sociocultural transformations of contemporary Iran while femiphobic attitudes have been central to this marginalization. Notwithstanding the profound distortion of the concepts of femininity and masculinity in the course of the modernization and islamization of the country, defeminization of the public space and prioritization of the masculinity in gender discourses have been crucial to all social transitions that intend to feed their desired social-ideal identity. Iranian homosexuals, who are condemned both for their sexuality and nonconformist gender effeminacy, have recently formed fictive kinships and backstage friendship groups in order to negotiate and attain a new social identity. In this paper I will examine the basic reasons behind the rejection of homosexuality in Iran and the ventures of the Iranian gays into cyberspace and back to society, while struggling to construct a new feminine-admissive social identity. The final part of this study is devoted to the discussion of the seat of femininity, particularly effeminacy, among Iranians. The paper concludes with demonstrating the incomplete social identity formation of gays, but also envisages a rising divergence in Iranian youths’ gender behaviors which grants the likelihood of negotiating the newly developed social identity to homosexuals.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.755
Threshold uncertainty score0.463

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 head0.059
GPT teacher head0.448
Teacher spread0.389 · 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 designObservational
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

Citations13
Published2016
Admission routes1
Has abstractyes

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