Is it Just Me? Queer Men's Negotiations of Queer Identities During Interactions With Heterosexual Cisgender Men
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.019 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".