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Record W2898829801 · doi:10.4312/as.24.3.11-28

Dripping pink and blue

2018· article· en· W2898829801 on OpenAlexaff
Darlene E. Clover, Nancy Taber, Kathy Sanford

Bibliographic record

VenueAndragoška spoznanja · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsBrock UniversityUniversity of Victoria
Fundersnot available
KeywordsPatriarchySociologyHegemonyNarrativeAgency (philosophy)Context (archaeology)Representation (politics)Opposition (politics)AestheticsGender studiesMedia studiesArtLiteratureSocial scienceHistoryLawPolitical sciencePolitics

Abstract

fetched live from OpenAlex

In response to calls by feminist cultural theorists to develop means to unmask patriarchy, the system of power that lies at the heart of museums that maintain problematic hierarchical binaries of masculinity and femininity, we designed the Feminist Museum Hack. The Hack draws on theories of representation, feminist critical discourse analysis and visual methodologies/literacy to operate as a critical and creative practice that can be adapted to any museum context. The primary aim of the Hack – a methodology and pedagogy – is to provide a lens through which adults can see the unseen of patriarchy and how it hides so cleverly in plain sight in the museum’s practices of representation. In this article, we use examples of how we have used the Hack as researchers and educators in various museum settings to expose, decode and disrupt the hegemonic gendered messages in the images, displays, curatorial statements, labels and even in object placement and stagecrafting. We also show how the Hack functions as a practice of ‘direct agency’, a means to re-write and engage with museum narratives. We argue that the Hack is an important and innovative practice because it turns museums into spaces of ‘pedagogic possibility’ – sites where we can learn new strategies of feminist opposition to counter the male gaze and its ability to define women’s lives.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.904
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.028
GPT teacher head0.217
Teacher spread0.189 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations8
Published2018
Admission routes1
Has abstractyes

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