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Record W6903407566 · doi:10.11575/prism/dspace/41022

Is it Just Me? Queer Men's Negotiations of Queer Identities During Interactions With Heterosexual Cisgender Men

2023· other· en· W6903407566 on OpenAlexaboutno aff

Bibliographic record

VenueOpen MIND · 2023
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsQueerNegotiationSociocultural evolutionContext (archaeology)Queer theoryThematic analysisQualitative research

Abstract

fetched live from OpenAlex

Queer men’s conceptualizations of their queer identities are multidimensional and cannot be captured by a singular definition. Similarly, queer men’s expressions of masculinities are equally as complex, as they depend on how they compete and interact with other masculinities. In this qualitative study, I examined how queer men negotiate their identities during interactions with heterosexual, cisgender men while considering the impacts that the sociocultural context of so-called Alberta has on these negotiations. I conducted semi-structured interviews with six queer men living in so-called Alberta to understand the behavioural implications of their interactions with het/cis men. Using thematic analysis, the findings suggested that queer men negotiate their behaviours in multiple ways by expressing queer masculinities to receive cultural and social benefits and safety from het/cis men. This is in response to het/cis men disproportionately creating uncomfortable interactional environments through their behaviours and the lack of representation within the so-called Alberta institutional and sociocultural context. However, queer men also employ queer masculinities by refuting negotiations of behaviour in response to het/cis men’s actions. This study emphasizes the impacts that behaviours, interactions, and culture have on queer men’s masculinities and identities that constantly shift, transform, and compete with other masculinities and identities.

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)0.005
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.133
Threshold uncertainty score0.265

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0170.019
Scholarly communication0.0050.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.098
GPT teacher head0.371
Teacher spread0.273 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2023
Admission routes1
Has abstractyes

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