“Oh! Based on Voice, Assigned Female at Birth”: Transmasculine Voices and Gender Construction
Bibliographic record
Abstract
The physical voice is one of the most noticeable gender signifiers utilized in everyday social interaction. For trans people, their voice can be a medium through which to affirm and assert their gender, or a source of dysphoria which regularly ‘betrays’ their identity to others. Because of the effects of masculinizing hormone replacement therapy (HRT), transmasculine people tend to have an easier time changing their voices to their desired pitch than transfeminine people. However, even when on testosterone, transmasculine people may feel pressure from both inside and outside their own communities to sound a certain way due to transnormative narratives that gain traction in mainstream media and online transmasculine spaces like Tumblr and YouTube. Transmasculine people who are nonbinary face further challenges asserting their identity through their voice due to having to frequently operate within the gender binary when it comes to gender membership. Through qualitative semi-structured interviews, I explore how two transmasculine participants view the relationships between their voice, gender identity, and the social world. I identify that trans people face pressures to sound a certain way from both cis- and transnormative lenses. I find that nonbinary transmasculine people particularly struggle with these pressures as having too high of a voice results in constant misgendering, and fears around both safety and keeping community arise with the threat of the “T-voice.”
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.014 | 0.028 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".