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Record W3211544466 · doi:10.1080/00918369.2021.1999122

Wired Sex Assemblages Among Men Who Have Sex with Men: Sexualized Drug Use, Hookup Apps, and HIV Service Provision

2021· article· en· W3211544466 on OpenAlexaffabout
Dave Holmes, Matthew Numer, Chad Hammond, Phillip Joy, Jad Sinno, San Patten, Marc‐André LeBlanc

Bibliographic record

VenueJournal of Homosexuality · 2021
Typearticle
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsMount Saint Vincent UniversityUniversity of SaskatchewanDalhousie UniversityUniversity of Ottawa
Fundersnot available
KeywordsMen who have sex with menSex workAssemblage (archaeology)Service providerGender studiesHuman immunodeficiency virus (HIV)SociologyDeleuze and GuattariService (business)PsychologyMedicineEcologyFamily medicineBusiness

Abstract

fetched live from OpenAlex

Sexualized drug use is a form of sexual practice that resists risk-based discourses (otherwise referred to as “radical sex practices”) and is reportedly common among gay, bisexual and other men who have sex with men (GBMSM). With the growth of online technologies, the use of hookup apps has also increased. We refer to men’s use of drugs, apps, and sex form as “wired sex” that forms what post-structuralist theorists Deleuze and Guattari described as an assemblage. Perspectives of the health and social service providers who work directly with GBMSM has not been explored. This research project involved a critical discourse analysis of 13 semi-structured interviews with service providers in Canada to understand their perspectives and interactions with wired sex assemblages. We identified several themes reflecting the social and political effects of wired sex assemblages and discuss the implications of these effects on services provision with GBMSM.

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)0.007
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 score0.288
Threshold uncertainty score0.573

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0190.023
Scholarly communication0.0070.004
Open science0.0010.007
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 head0.031
GPT teacher head0.326
Teacher spread0.295 · 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

Citations8
Published2021
Admission routes2
Has abstractyes

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Same venueJournal of HomosexualitySame topicSexuality, Behavior, and TechnologyFrench-language works237,207