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Record W4253735283 · doi:10.32920/ryerson.14652792

Fluid: a critical examination of queer artists and their clothing choices

2021· preprint· en· W4253735283 on OpenAlexaboutno aff
Alysia Myette

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicFashion and Cultural Textiles
Canadian institutionsnot available
Fundersnot available
KeywordsQueerClothingSociologyResistance (ecology)AestheticsQueer theoryGender studiesVisual artsArtHistory

Abstract

fetched live from OpenAlex

Fluid is a documentary that explores how self-identifying queer artists self-fashion and engage in cultural production as a two-fold approach to queer worldmaking. The documentary presents the ideas and sentiments of six Toronto based queer artists towards their practice and their clothing choices through a multidisciplinary practice led methodology. Using filmed and audio recorded interviews, Fluid centers the voices of intersectional identities in queer fashion studies. Fluid is the key component of a larger major research project (MRP) that seeks to discover how queer artists engage in queer worldmaking through sartorial choice making, artistic practice and self-expression. Through a series of documented interviews, reflection and analysis, this MRP sheds light on themes of gender performance, resistance, queer failure and survival in the self-fashioning of queer artists. This MRP also makes visible how these themes manifest as cultural production and as an extension of their creative practice.

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)0.007
metaresearch head score (Gemma)0.016
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 score0.057
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.003
Science and technology studies0.0230.035
Scholarly communication0.0110.007
Open science0.0010.007
Research integrity0.0020.004
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 head0.049
GPT teacher head0.266
Teacher spread0.217 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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