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Record W4395479251 · doi:10.1007/s12119-024-10231-1

Dating Apps and Shifting Sexual Subjectivities of Men Seeking Men Online

2024· article· en· W4395479251 on OpenAlexafffund
Barry D. Adam, David J. Brennan, Adam Davies, David Collict

Bibliographic record

VenueSexuality & Culture · 2024
Typearticle
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsUniversity of GuelphUniversity of TorontoHolland Bloorview Kids Rehabilitation HospitalUniversity of Windsor
FundersCanadian Institutes of Health ResearchCanadian Foundation for AIDS Research
KeywordsFriendshipGender studiesHuman sexualityNarrativeTemporalitiesPsychologySociologySocial psychologyDemocratizationPolitical scienceArt

Abstract

fetched live from OpenAlex

Leading theories of the recent history of sexuality have pointed to trends toward detraditionalization and precarity in intimate relations, but also to democratization and innovation. This study grounded in 79 qualitative interviews with men seeking men online considers their experiences in light of these theories. The rise of dating apps has generated sexual fields that have shaped the sexual subjectivities of the current era in multiple ways. The narratives of study participants show much more than the hook-up culture that dating apps are best known for. They speak to experiences of superficiality, unmet expectations, and sometimes bruising intersections with hierarchies defined by age, race, body type, gender expression, and serostatus. Yet at the same time, they show a strong aspiration to sociability, social network building, and reach for a language of affiliation beyond the kin and friendship terms of the larger society. Generational comparisons indicate the shifting sexual subjectivities that dating apps have shaped by constituting virtual sexual fields.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0040.002
Open science0.0000.003
Research integrity0.0010.001
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 head0.044
GPT teacher head0.364
Teacher spread0.320 · 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

Citations5
Published2024
Admission routes2
Has abstractyes

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