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Record W3080905057 · doi:10.1080/13500775.2020.1743017

The National Museum of Women in the Arts and the Museum of Women: Preserving Women’s Heritage and Empowering Women

2020· article· en· W3080905057 on OpenAlexaboutno aff
Julie Botte

Bibliographic record

VenueMuseum International · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical Gender and Feminism Studies
Canadian institutionsnot available
Fundersnot available
KeywordsExhibitionMuseologyMuseum informaticsThe artsNational museumWomen's historyFeminismSociologyNational heritageVisual artsGender studiesHistoryAnthropologyArt

Abstract

fetched live from OpenAlex

The National Museum of Women in the Arts in Washington, D.C., in the United States, and the Museum of Women (Musée de la Femme) in Longueuil, Canada, were founded with the objective of making women visible in museum collections and promoting gender equality in society. Can they be considered feminist museums? Feminist museology is not limited to describing the past and the present; on the contrary, it raises questions and paves the way for an egalitarian future. A feminist museum aims to raise awareness and change mentalities. Despite their shared ambition of acting on behalf of women with the help of a museum, the National Museum of Women in the Arts and the Museum of Women reflect two different approaches. A comparison of these case studies sheds light on two different solutions for integrating gender into museums. How do these two museums fulfil their missions through their permanent collections and temporary exhibitions? How does a museum become a means for encouraging social change? Their collections raise awareness of women's history and offer new interpretations of objects. Both museums try to change the way visitors look at the past and contemporary society by offering different readings of history and art history. Despite their differences, both make visitors aware of gender inequality in museums and the world.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.308
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.034
GPT teacher head0.300
Teacher spread0.267 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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