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Record W4313855620 · doi:10.26443/crae.v49i1.287

Volume 49, No. 1: Art Education- Virtual Material

2022· article· en· W4313855620 on OpenAlexaffvenueabout
Adrienne Boulton

Bibliographic record

VenueThe Canadian Review of Art Education / Revue canadienne d’éducation artistique · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsVolume (thermodynamics)Computer graphics (images)Computer sciencePhysicsThermodynamics

Abstract

fetched live from OpenAlex

eaders of this issue of the Canadian Review of Art Education (CRAE) will find five research articles and a large Salon section dedicated to the works of Canadian artists.The first two articles take up art education research in the museum context.Jacob Le Gallais examines the complexity of his childhood memory of museum taxidermy by exploring the nuanced ways art making might confront the seemingly mundane naturalistic display of animals.This work challenges us to critically examine broader discourses surrounding colonialism and the fetishization of nature through an analysis of museological practices, the display of animal bodies, and critical acts of making.Le Gallais' panels Crane Collage 1 [left panel] and Crane Collage 2 [right panel] compose this edition's cover image, and readers are invited to read his discussion of this work in the Artist's Statement.Emma June Huebner examined the use of social media, particularly Instagram and IGTV, as instrumental components in how museum educators engaged virtual visitors during the Covid 19 Pandemic.Huebner looked at how museums used technology, including Instagram and IGTV, to connect viewers to the gallery shows through the materiality of art making demonstrations and lessons posted by the museums.She examined the implications of the loss of material space and the use of technology in broader discourses of gendered work in museological practices.The last three articles explore facets of art teaching practices for their impact on student learning in and through the arts.Barbara Hirst provides a generative discussion of the relational and subjective experience of time through Heidegger's philosophy of time perception.She connects these understandings to how students with ADHD experience time to offer insights into art instruction informed by students' temporal perceptions and the phenomenology of art experience.Tiina Kukkonen and Benjamin Bolden draw on Teresa Amabile's Componential Theory of Creativity for its potential in nurturing existing teaching practices to further support creative development in the art classroom.Marie-Pierre Labrie explores the generative force of a dialogue between design-based research and research-creation in art education.She argues the intricacy of the creative process occurring in art-making may contribute to the methodological endeavours of design-based research.Starting with a theoretical standpoint, the author seeks the two methodologies' epistemological and procedural meeting points.The Salon section features the artists and works of the CSEA/SCEA 2021 virtual exhibit Navigating and Creating, curated by Dr.

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.940
Threshold uncertainty score0.998

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0070.018
Science and technology studies0.0050.008
Scholarly communication0.0140.006
Open science0.0020.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0600.007

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 head0.018
GPT teacher head0.241
Teacher spread0.223 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2022
Admission routes3
Has abstractyes

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