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Record W4394985934 · doi:10.1080/02601370.2024.2341761

Unearthing a hidden curriculum of gendered museum languages through critical feminist visual discourse analysis

2024· article· en· W4394985934 on OpenAlexafffundabout
Darlene E. Clover, Kathy Sanford

Bibliographic record

VenueInternational Journal of Lifelong Education · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsUniversity of Victoria
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCritical discourse analysisSociologyDiscourse analysisCurriculumCritical theoryGender studiesPedagogyLinguisticsEpistemologyPolitical sciencePolitics

Abstract

fetched live from OpenAlex

This article explores the hidden gendered curriculum concealed in the texts, exhibitions and other structural devices of museums and what they teach visitors to see and think. Grounded in conceptualisations of culture, representation, and language, past feminist studies of museums and applying feminist visual discourse analysis (FVDA) we found hiding in plain sight in public museums in Canada and England a diversity of ‘languages’ that subversively aggrandised masculine power and privilege and (re)inscribed negative or limited understandings to women which we argue as feminist adult educators have epistemic, identity and agency consequences. We also argue that whilst museums are trying to address often centuries old exclusionary gendered practices progress is slow because these practices are so embedded and unconscious and reflect pervasive social notions of ‘common sense’. Our study makes an important contribution to feminist museum studies by rendering visible a web of complex structural and textual gendered biases and to adult education. By adding museums to our curricula we can provide our students with a visual and immersive way to hone the critical visual literacy skills needed to understand more fully how both seen and unseen languages operate not just in the museum, but across society.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.399
Teacher spread0.371 · 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 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

Citations2
Published2024
Admission routes3
Has abstractyes

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