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Record W2890302810 · doi:10.1386/jptv.6.3.339_1

From ambivalence to acceptance: Representations of trans embodiment on American television

2018· article· en· W2890302810 on OpenAlexaff
Marc Lafrance, Jay Manicom, Geoff Bardwell

Bibliographic record

VenueThe Journal of Popular Television · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsUniversity of British ColumbiaConcordia University
Fundersnot available
KeywordsAmbivalenceTelevision seriesProsperityReality televisionAestheticsSociologySocial psychologyBeautyPsychologyGender studiesMedia studiesArtPolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract Boasting some of the highest ratings in American cable television history, FX’s six-season dramatic series Nip/Tuck (2003–10) features more trans characters than any other show of its kind. Focusing on its regularly recurring trans woman character, Ava Moore, we argue that Nip/Tuck’s representations of trans embodiment are complex, contradictory and, above all, ambivalent. More specifically, we claim that the show portrays medically-assisted transition not only as a large-scale crisis in the order of things but also as a path to personal prosperity and success. In doing so, we demonstrate that the former is articulated through themes of incest, infertility, fraudulence and monstrosity while the latter is articulated through themes of beauty, intelligence, resilience and social mobility. Having presented our analysis of how Nip/Tuck represents sex reassignment and those who undergo it, we then turn to a critical consideration of how a more recent dramatic series, Orange is the New Black (2013–present), portrays the trans trajectory. Widely understood to represent a kind of ‘transgender tipping point’, Orange is the New Black can be seen as a useful index of how trans people are portrayed in present-day televisual culture. Through our consideration of these portrayals, we think critically about whether popular media representations of sex reassignment have changed since Nip/Tuck and, if so, in what ways.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.498
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.034
GPT teacher head0.369
Teacher spread0.335 · 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 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

Citations4
Published2018
Admission routes1
Has abstractyes

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