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Record W3109212999 · doi:10.3167/armw.2020.080112

Interruptions: Challenges and Innovations in Exhibition-Making

2020· article· en· W3109212999 on OpenAlexaff
Laura Osorio Sunnucks, Nicola Levell, Anthony Shelton, Motoi Suzuki, Gwyneira Isaac, Diana E. Marsh

Bibliographic record

VenueMuseum Worlds · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsReflexivityMuseologySociologyEnlightenmentContext (archaeology)ExhibitionPopulismRacismCritical theoryEpistemologyEnvironmental ethicsPolitical scienceSocial scienceGender studiesPoliticsHistoryLaw

Abstract

fetched live from OpenAlex

Anthropology and its institutions have come under increased pressure to focus critical attention on the way they produce, steward, and manage cultural knowledge. However, in spite of the discipline’s reflexive turn, many museums remain encumbered by Enlightenment-derived legitimating conventions. Although anthropological critiques and critical museology have not sufficiently disrupted the majority paradigm, certain exhibitionary projects have served to break with established theory and practice. The workshop described in this article takes these nonconforming “interruptions” as a point of departure to consider how paradigm shifts and local museologies can galvanize the museum sector to promote intercultural understanding and dialogue in the context of right-wing populism, systemic racism, and neoliberal culture wars.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.050
metaresearch head score (Gemma)0.054
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.264

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0500.054
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0120.053
Scholarly communication0.0290.028
Open science0.0070.020
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0080.001

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 head0.115
GPT teacher head0.266
Teacher spread0.151 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2020
Admission routes1
Has abstractyes

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