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Record W2735284598 · doi:10.18733/c3g66r

Us-Them-Us: Artists Interrogate the Ambivalent Structures of Belonging

2017· article· en· W2735284598 on OpenAlexafffundvenueabout
Jennifer Eiserman

Bibliographic record

VenueCultural and Pedagogical Inquiry · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsUniversity of Calgary
FundersUniversity of Alberta
KeywordsExhibitionCurriculumAmbivalenceIdentity (music)NormativeSociologyAction (physics)ChoreographyVisual artsArtMedia studiesPedagogyAestheticsPolitical sciencePsychologyLawDanceSocial psychology

Abstract

fetched live from OpenAlex

Us-Them-Us was an exhibition held as part of the pre-conference to the Social Sciences and Humanities Congress 2016 of the Comparative and International Education Society of Canada at the University of Calgary, Alberta. The exhibition included the work of seven artists whose work engage in discourses surrounding identity and belonging. Their works disrupt the normative implicit curriculum of art education with its western, patriarchal bias. They open spaces to explore the intricate choreography that is required to be part of a society. This essay introduces the works in Us-Them-Us that form the visual essays included in this special issue. Each work pulls back the layers of the complex problem. Taken as a whole, they expose the implicit curricula that a society imposes on its members in order that they learn to belong. Keywords: Implicit Curricula; Art Education; Art as Social Action

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)0.006
metaresearch head score (Gemma)0.011
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 score0.014
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0140.044
Scholarly communication0.0130.007
Open science0.0010.008
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0030.000

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 head0.602
GPT teacher head0.446
Teacher spread0.155 · 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

Citations0
Published2017
Admission routes4
Has abstractyes

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