"Authentically" Effeminate? Bialystok's Theorization of Authenticity, Gay Male Femmephobia, and Personal Identity
Bibliographic record
Abstract
Authenticity is a commonly heralded ideal in Western modernist discourses, with a large amount of literature describing individuals’ personal journeys towards self-fulfillment (Bialystok, 2009, 2013, 2015, 2017; Taylor, 1991; Varga, 2014). This paper examines Lauren Bialystok’s (2013) conception of authenticity in sex/gender identity and proposes that effeminate or ‘femme’ gay men make a strong case for fitting within such a conception of authenticity. Effeminate gay men experience significant in-group discrimination within gay men’s communities, with many gay men “defeminizing” (Taywaditep, 2002) themselves upon entering adulthood and mainstream gay communities. Through this exploration of Bialystok’s (2013) model for authenticity in sex/gender identity and the identity-based challenges effeminate or femme gay men experience, this paper describes why effeminate gay men fit Bialystok’s model, and the ethical dilemmas of theorizing authenticity in personal identity (Bialystok, 2009, 2011). Providing supportive and positive early environments in school while specifically addressing gender-based discrimination in childhood provides more opportunities for positive identity development and the potential of fulfilling self-authenticity within gender identity for femme gay men.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".