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Record W2136400063 · doi:10.3828/jlcds.2014.25

Cripping the Museum

2014· article· en· W2136400063 on OpenAlexaff
Sarah Brophy, Janice Hladki

Bibliographic record

VenueJournal of Literary & Cultural Disability Studies · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsMcMaster University
Fundersnot available
KeywordsExhibitionPraxisPoeticsArgument (complex analysis)Video artPerspective (graphical)ArtAestheticsVisual cultureVisual artsDiversity (politics)SociologyArt historyLiteraturePhilosophyAnthropologyEpistemologyMedicinePerformance artPoetry

Abstract

fetched live from OpenAlex

In November 2010, there was a conference on "Health, Embodiment, and Visual Culture" at McMaster University and an exhibition called "Scrapes: Unruly Embodiments in Video Art" at the McMaster Museum of Art. This article pursues the idea and implications of "scrapes" and "to scrape": as a framework of disturbance and rupture; for knowing differently from a crip perspective; and for cripping museological praxis. Engaging closely with the visual and aural poetics, and critical activist rhetorics of the video works, the argument is that their unruly embodiments generate a mutually informing unsettlement of ableist, heteronormative, and neocolonial relations. The article also reflects critically on the potential to crip the museum when the exhibition's video art archive rubs up against the regulatory mechanisms and the neoliberal model of diversity in the un-cripped university.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.020
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0170.022
Scholarly communication0.0120.010
Open science0.0020.025
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0200.002

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 head0.308
Teacher spread0.216 · 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

Citations7
Published2014
Admission routes1
Has abstractyes

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