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Record W4412899859 · doi:10.1080/02660830.2025.2538385

Looking back to think forward: an exploration of what and how feminist exhibitions educate

2025· article· en· W4412899859 on OpenAlexafffundabout
Darlene E. Clover

Bibliographic record

VenueStudies in the Education of Adults · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsUniversity of Victoria
FundersSocial Sciences and Humanities Research Council
KeywordsExhibitionSociologyFeminismGender studiesPolitical scienceVisual artsArt

Abstract

fetched live from OpenAlex

Curating temporary exhibitions with high visual appeal and compelling historical narratives is central to the work of museums. Yet what exhibitions teach as historical truth tends to concentrate on heteronormative masculine histories, reinforcing superiorities and whitewashing centuries of patriarchal oppression, control and violence. In response, museums in Canada and England are curating feminist exhibitions. I explored how 14 feminist exhibitions curated between 2016 and 2024 operated as feminist adult educators and specifically, what and how they educated. Findings how critical and creative strategies ‘herstory’ new subjects and agency, disrupt universalisms, make visible common differences, illuminate hidden ideologies, and out historical myths that continue to shape our gendered world. Feminist exhibitions not only minimise patriarchal power but interrupt negative perceptions of feminists through humour, satire and irony. By showcasing a range of issues, power dynamics and lives long denied visibility and intellectual credibility feminist exhibitions provide role models, create new knowledge and make a different world visible, hearable, feelable, and imaginable. By looking back they think forward, by cross fertilising the past with the present they offer not simply a new women’s history but a new history for all.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.214
Threshold uncertainty score0.214

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)0.0000.000

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 head0.073
GPT teacher head0.337
Teacher spread0.264 · 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.

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
Published2025
Admission routes3
Has abstractyes

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