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Record W4416679959 · doi:10.24043/001c.147179

In the Dark: Queer Male Social-Sexual Encounters in Dark Spaces

2025· article· en· W4416679959 on OpenAlexaff
Nick J. Mulé

Bibliographic record

VenueFolk, knowledge, place. · 2025
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsYork University
Fundersnot available
KeywordsQueerHuman sexualitySubalternOppressionImpermanenceHeteronormativityMainstream

Abstract

fetched live from OpenAlex

Male-to-male social-sexual activity in the subaltern world of male sexual spaces is theoretically examined. Methods include hard copy and online content analysis and observational-participatory submergence in the subaltern world of queer male sexual spaces such as bathhouses, saunas, circuit clubs, fetish balls, sex clubs, dark rooms, and backrooms. Studied is a self-monitored subculture that creates its own tribal rituals at varying odds with mainstream societal and LGBTQ movement norms. Darkness is a common conceptual theme in such spaces serving multiple purposes from anonymity to atmospheric shrouding, from sensory deprivation to expanded imaginaries, from lowered inhibitions to sexual exploration. The importance of such spaces is examined regarding time-limited sexual expression for pleasure and affirmation. This contrasts greatly from normative societal expectations, partly due to sex and sexuality being core to queer culture and due to ongoing oppression towards queer men. Transgressive spaces designed as queer male social-sexual places serve several socio-cultural needs materially, allowing for perceptive liberation, figurative creative personas that symbolize one’s authentic being. Public and private spheres are somewhat blurred, yet through social etiquette navigable. By deviating from and resisting social-sexual norms, this tribe demonstrates how it maintains a core drive of their liberated sexuality outside of mainstreamed sexual governance.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.370
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.025
GPT teacher head0.375
Teacher spread0.350 · 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 designNot applicable
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

Citations1
Published2025
Admission routes1
Has abstractyes

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