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Record W4316362632 · doi:10.1177/00084298221128883

Complicit silence, fluid identities and a shift to personalized faith: LGBTQ+ experiences in conservative Christian communities

2023· article· en· W4316362632 on OpenAlexaffvenueabout
Kelsey Block

Bibliographic record

VenueStudies in Religion/Sciences Religieuses · 2023
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsQueerTransgenderGender studiesLesbianSilenceSexual identitySociologyHomosexualityHuman sexualityContext (archaeology)Queer theoryLiminalityIdentity (music)GeographyAestheticsAnthropology

Abstract

fetched live from OpenAlex

Using in-depth interviews with six participants, this qualitative project examines LGBTQ+ (lesbian, gay, bisexual, transgender, queer and others) experiences in conservative Christian communities in British Columbia and Alberta, Canada, through the lens of queer theory. The research questions guiding this project are: (1) Does there continue to be a code of silence surrounding LGBTQ+ identities within conservative Christian communities? (2) How do LGBTQ+ individuals deal with the perceived incompatibility between their religion and their sexuality and/or gender? (3) How do LGBTQ+ individuals understand their LGBTQ+ identity when situated within a traditionally heteronormative religious community? The findings indicate that the participants view the silence surrounding LGBTQ+ issues and the subsequent lack of formal support for LGBTQ+ individuals as complicit in perpetuating rhetoric that LGBTQ+ identities are abnormal, sinful and shameful. All of the participants shifted to a more personalized faith and view Christianity as a resource instead of a requirement, and the majority of the participants frame both their gender/sexual identity and religious identity as fluid and liminal, subject to change depending on the context.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.315
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.181
GPT teacher head0.464
Teacher spread0.283 · 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 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

Citations7
Published2023
Admission routes3
Has abstractyes

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