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The Feminist Museum Hack as an aesthetic practice of possibility

2019· article· en· W2915664881 on OpenAlexaffabout
Darlene E. Clover, Sarah Williamson

Bibliographic record

VenueEuropean Journal for Research on the Education and Learning of Adults · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsNarrativeAgency (philosophy)SociologyObjectificationInvocationInstitutionVisual artsMuseum educationMedia studiesAestheticsPedagogyArtPolitical scienceLawLiteratureSocial scienceAnthropology

Abstract

fetched live from OpenAlex

This article outlines the central components, foundations and key activities of the Feminist Museum Hack, an investigative, pedagogical, analytical and interventionist tool we have designed to explore patriarchal assumptions behind the language, images and stragecrafting (positioning, lighting) of museums and art galleries. We also share findings from a study of student and community participants who employed the Hack in a museum in Canada and an art gallery in England. While differences existed due to institutional genres, findings showed participants’ ability to see and to reimagine absences, objectification, fragmentation, and double-standards and apply these to the world beyond the institution’s walls. As a form of pedagogy of possibility, the Hack encourages critique, just ire and the imagination. As it hones visual literacy skills it emboldens participants to challenge the authority of the museum narratives and to engage in creative practices of agency and activism.

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)0.006
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 score0.011
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.052
Scholarly communication0.0080.009
Open science0.0010.017
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.000

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 head0.083
GPT teacher head0.400
Teacher spread0.318 · 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
Published2019
Admission routes2
Has abstractyes

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