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Record W4400820487 · doi:10.1080/03612112.2024.2367372

Increasing Mannequin Diversity in Museum Exhibitions to Address Social Equity

2024· article· en· W4400820487 on OpenAlexaboutno aff
Anne Bissonnette

Bibliographic record

VenueDress · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsnot available
Fundersnot available
KeywordsExhibitionEquity (law)Diversity (politics)Visual artsSociologyPublic relationsArtPolitical scienceAnthropologyLaw

Abstract

fetched live from OpenAlex

For too long, museums have used thin, white mannequins as the standard for body surrogates in dress and textile exhibitions. This article explores some of the issues surrounding this practice and the message these objects may convey. As an actionable step towards social equity, it offers an alternative called the “Forest People” that aims to address issues of diversity and inclusion in curatorial and display practices. Inspired by tree diversity, the Forest People is a solution that draws from the past to create a new version of artists’ wooden lay figures. It replicates different wood colors and grains and can lend itself to presenting a variety of different races while still fading in the background and conveying an aesthetically pleasing and visually coherent ensemble. The article reflects on the development of a prototype for the exhibition Woven Identities: Ghanaian and Guatemalan Textiles in the Face of Globalization (2023) at the University of Alberta, and its effectiveness and potential developments.

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 score0.888
Threshold uncertainty score0.998

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.127
GPT teacher head0.316
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

Citations1
Published2024
Admission routes1
Has abstractyes

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