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“Oh! Based on Voice, Assigned Female at Birth”: Transmasculine Voices and Gender Construction

2023· article· en· W4387434277 on OpenAlexaffvenue
Adrian Dunkerson, Anelyse M. Weiler

Bibliographic record

VenueCanadian Graduate Journal of Sociology and Criminology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsGender dysphoriaMainstreamTransgenderIdentity (music)NarrativeSocial mediaPsychologyGender identityComputer scienceSocial psychologyGender studiesSociologyLinguisticsAestheticsWorld Wide Web

Abstract

fetched live from OpenAlex

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.”

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.312
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.158
GPT teacher head0.322
Teacher spread0.164 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations2
Published2023
Admission routes2
Has abstractyes

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