Anatomy of a Gender: A Micro-Example (Or, An Autotheoretical Disruption of Societal Demands for Trans Legibility)
Bibliographic record
Abstract
In this thesis, I employ autotheory, critical discourse analysis, and interview-based research to address the question of how, even as trans acceptance grows more common, normative scripts of (trans)gender still limit and marginalize trans people who are additionally queer, gender nonconforming, and/or nonbinary.Centring my lived embodied experiences as a trans gender nonconforming man, I demonstrate that gender can be far more unknowable and incoherent than any single definition of trans experience can hope to describe.In so doing, my thesis contributes to debates within trans studies concerning the legibility of individuals whose gender identities and expressions challenge the rigidity of the binary logic present in cisgenderism and transnormative politics.Furthermore, my making visible some of the struggles that nonbinary and/or gender non-conforming trans people go through to live an authentic life contributes trans politics beyond the academy.enough to merit inquiry and thus define as gender-related-it's less cinematic, by far, so I can hardly begrudge its exclusion from the dominant narrative.1 This project was borne out of my own difficulties as a gender non-conforming trans person: I am a trans man who is often feminine in aesthetic and behaviour.My motivation originates with my frustration that my experience as a trans person is not reflected by the culture around me, nor recognized as legitimate by a society that has a very strict, gender normative understanding of transgender identity that is largely relegated to material dimensions-both selfstyling as masculine or feminine, and the role of medical transition procedures to 'create' a 'properly' sexed body.Being white and transmasculine, once again, I experience privilege that others do not, and thus my ability to access legal and social recognition has not been significantly impaired by my dissonant gender presentation.I have grappled, however, with years of internalized transphobia emerging from the inability to clearly define myself, as regulatory discourses about gender advance the impression that gender is supposed to be simple-there are only two, after all, and if there is any confusion, a look in the mirror should clear things up! 2 Throughout this thesis, I may use a legion of terms to refer to my own gender.Some of 3 these terms may read as contradictory and with varying levels of crassness: trans man, trans masculine, trans male, genderqueer, nonbinary, FTMT+ (Female-To-Male-To-+, the "+" referring to the one used in the acronym "LGBTQ+"), transsexual, girlyboy, femboy, transfag, These brief poetic observations, justified right and intentionally struck out, are one of my 1 strategies to disrupt the academic insistence upon the cohesive organization of a text, which I will unpack more clearly in my methods section.I am begging my reader to understand that I make this statement in the most sarcastic possible 2 manner."[…] for we are many."(English Standard Version Bible, 2001, Mark 5:9) 3
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".