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Record W3202939908 · doi:10.1111/nup.12370

Assemblages of excess and pleasures: The sociosexual uses of online and chemical technologies among men who have sex with men

2021· article· en· W3202939908 on OpenAlexafffund
Matthew Numer, Dave Holmes, Chad Hammond, Phillip Joy, Jad Sinno

Bibliographic record

VenueNursing Philosophy · 2021
Typearticle
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsMount Saint Vincent UniversityUniversity of SaskatchewanUniversity of OttawaDalhousie University
FundersInstitute of Infection and ImmunityCanadian Institutes of Health Research
KeywordsTechnoscienceRecreationPleasureQueerThematic analysisGender studiesNegotiationSociologyMen who have sex with menVariety (cybernetics)PsychologyQualitative researchPolitical scienceSocial scienceMedicineComputer science

Abstract

fetched live from OpenAlex

Chemicals have penetrated everyday lives of men who have sex with men as never before, along with new online and mobile technologies used to seek pleasures and connections. Poststructuralist (including queer) explorations of these new intensities show how bodies exist in the form of (political) surfaces able to connect with other bodies and with other objects where they may find/create a function (e.g., reproduce or disrupt hegemonies). This federally funded netnographic study explored how a variety of chemicals such as recreational drugs, pharmaceuticals and steroids are contributing to the construction of gay, bisexual and other men having sex with men (GBMSM) communities and their interactions with idealized masculinities in the age of increasing technology. Five major thematic categories emerged from our analysis: (1) assembling bodies and technologies, (2) becoming orgiastic, (3) experiencing stigma, (4) becoming machinic and (5) negotiating practices. Our analysis explores how and why GBMSM pursue excesses of pleasure and connection through the assemblages they make with sexualized drug use, online platforms and other men.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.328
Threshold uncertainty score0.872

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 studies0.0000.002
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 head0.050
GPT teacher head0.346
Teacher spread0.296 · 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

Citations6
Published2021
Admission routes2
Has abstractyes

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