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Record W2972920710 · doi:10.3138/topia.2019-0004

“I (Don’t) Want To Be Seen: A Performative Auto-Ethnography of the Young Feminist Artist in Public”

2019· article· en· W2972920710 on OpenAlexaffvenueabout
Lauren Fournier

Bibliographic record

VenueTOPIA Canadian Journal of Cultural Studies · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicArtistic and Creative Research
Canadian institutionsSocial Sciences and Humanities Research CouncilUniversity of Toronto
Fundersnot available
KeywordsPerformative utteranceEthnographySociologyHarassmentPublic spaceAestheticsTRACE (psycholinguistics)Space (punctuation)Gender studiesVisual artsArtMedia studiesAnthropologyPolitical scienceLawComputer science

Abstract

fetched live from OpenAlex

In this article, I bring together three auto-ethnographic vignettes, written between 2011 and 2014, that converge around issues of public space, feminist cultural production, technology, and physical exposure in Vancouver and Berlin. Drawing from performance studies frameworks, I position both the rituals of everyday life (like ordering an Americano from a coffeeshop in Vancouver) and moments of explicitly making an artwork (like collaborating on a performance for Vancouver’s LIVE 2011 biennale) as performative. What began as an attempt to read a Vancouver coffeeshop through a fairly straightforward, auto-ethnographic, de Certeauian framework during my Masters research evolved into a scene of sexual harassment and assault, and this article is both a trace of, and a testament to, the challenges that young women-identifying artists and scholars continue to come up against when they do their work in public.

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)0.004
metaresearch head score (Gemma)0.007
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 score0.201
Threshold uncertainty score0.400

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0330.029
Scholarly communication0.0110.004
Open science0.0020.007
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.092
GPT teacher head0.305
Teacher spread0.214 · 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
Published2019
Admission routes3
Has abstractyes

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