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Record W3112847158 · doi:10.1093/jcmc/zmaa016

Identity Collision: Older Gay Men Using Technology

2020· article· en· W3112847158 on OpenAlexfundno aff
Avi Marciano, Galit Nimrod

Bibliographic record

VenueJournal of Computer-Mediated Communication · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSharing Economy and Platforms
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsHomosexualityAlienationHuman sexualityIdentity (music)Information and Communications TechnologyGender studiesPsychologyThematic analysisSociologySocial psychologyAestheticsQualitative researchPolitical scienceSocial science

Abstract

fetched live from OpenAlex

Abstract This study examines identity work among older gay men in relation to Information and Communication Technology (ICT). It draws on the notion of IT identity—the extent to which individuals experience technology as integral to their sense of selves—to explore how their homosexuality and advanced age shape their relationships with technology. Applying thematic analysis to 17 semi-structured, in-depth interviews with gay male users aged 66–81, we show that while homosexuality and technology enable and reinforce one another, the relationship between technology and advanced age can be better defined by alienation and estrangement. Consequently, we argue that technology constitutes a crossroads at which the gay and elder identities intersect and collide. In this sense, technology is similar to other cultural constructs, like sexuality, that challenge the merger of advanced age and homosexuality, rendering the older gay identity almost impossible.

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)0.005
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.004
Scholarly communication0.0060.004
Open science0.0000.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.037
GPT teacher head0.269
Teacher spread0.232 · 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

Citations18
Published2020
Admission routes1
Has abstractyes

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