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Record W3199700946 · doi:10.3983/twc.2021.1893

Zankie, queerbaiting, and performative rhetorics of bisexuality

2021· article· en· W3199700946 on OpenAlexaff
Xavia A. Publius

Bibliographic record

VenueTransformative Works and Cultures · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHeteronormativityPerformative utteranceRhetoricQueerNarrativeContext (archaeology)ConversationSociologyScrutinyLesbianIdentity (music)AestheticsGender studiesSubversionMedia studiesArtLiteratureHistoryLawPolitical sciencePhilosophyPoliticsTheology

Abstract

fetched live from OpenAlex

In 2014, two contestants on Big Brother (CBS), Frankie Grande and Zach Rance, began a showmance (ship name: Zankie). The presence of two men in a showmance, only one of whom was openly queer before filming, created ample conversation among fans and contestants about Rance's sexual orientation, as he seemed to be undergoing a personal bi-awakening narrative on live TV. The rhetorics of reality TV paint this both as a sincere struggle and as a joking game strategy, which occasions an overdetermined scrutiny of whether Rance is really bisexual or if he is queerbaiting the audience. Rance's performance of self on the show relies on queerbaiting, but he also deploys rhetoric surrounding bisexuality that allows him to participate in a same-sex showmance while still claiming heterosexuality outside the context of the show. His contradictory articulations of identity and desire reinforce stereotypes about bisexuals while also calling into question the heteronormative assumptions behind the showmance label.

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)0.003
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0130.031
Scholarly communication0.0080.006
Open science0.0010.004
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0050.000

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 head0.023
GPT teacher head0.325
Teacher spread0.301 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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