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Record W3182661769 · doi:10.33137/twpl.v43i1.35968

Both, and: Transmedicalism and resistance in non-binary narratives of gender-affirming care

2021· article· en· W3182661769 on OpenAlexaffvenue
Lex Konnelly

Bibliographic record

VenueToronto Working Papers in Linguistics · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGender Studies in Language
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTransgenderGender dysphoriaBinary oppositionNarrativeSalience (neuroscience)Resistance (ecology)DistressPsychological interventionPsychologyPerspective (graphical)Health careDysphoriaSociologySocial psychologyGender studiesClinical psychologyPolitical scienceEpistemologyLinguisticsComputer sciencePsychiatryCognitive psychology

Abstract

fetched live from OpenAlex

While gender dysphoria is a real and acute distress for many transgender people, it is not universal, and it is experienced and oriented to in a myriad of ways. However, its status as a prerequisite for gender-affirming care can lead trans people to feel compelled to amplify its salience in their pursuits for medical support. Through a critical discourse analysis of non-binary healthcare narratives, I trace the relationship between linguistic practices in these care interactions and the gender and sexual logics of the transmedicalist model of transgender care. With a focus on excerpts that center on individuals’ descriptions of dysphoria in the consultation room, I contend that these experiences are not straightforward accounts of assimilation to transmedicalist expectations. Rather, when read from a trans linguistic perspective, these strategies are examples of non-binary patients enacting their own interventions on a process over which (it may seem) they have minimal control and present a critical thirding (Tuck 2009) of a dichotomous view of either transnormativity or resistance.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.056
Scholarly communication0.0090.010
Open science0.0010.010
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.307
Teacher spread0.287 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations17
Published2021
Admission routes2
Has abstractyes

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Same venueToronto Working Papers in LinguisticsSame topicGender Studies in LanguageFrench-language works237,207