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Record W4292488352 · doi:10.1111/jade.12430

Intra‐spective Event‐encounters in Museums: A Pedagogic Practice Among Community Art Educators in Training

2022· article· en· W4292488352 on OpenAlexfundno aff
Patricia Osler, Anita Sinner, Lara El Tannir

Bibliographic record

VenueInternational Journal of Art & Design Education · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCuriosityIntrospectionPsychologyRelation (database)ReflexivityPedagogyInterpretation (philosophy)Visual artsAestheticsSociologySocial psychologyArtCognitive psychology

Abstract

fetched live from OpenAlex

Abstract This article reflects understandings of artful encounters in relation to ecologies of practice and walking with public art. Introduced as a pedagogic inquiry among art education students training to become teachers in community settings, meaningful event‐encounters transpired while walking with the museum. Navigating spaces of becoming‐with between body‐object‐environment encouraged relational positions: self as a/r/tographer, self in relation to artworks, self in relation to a museum space and self in relation to co‐participants. Visual, spoken and written responses to artworks were anchored in guiding themes and in an awareness of the artist's creative process. The resultant perceptions are defined in this case as introspective, extrospective and what may be described as intra‐spective (reflecting the experience of collaborative exploration). Participant data revealed that the exchange of interpretation becomes a meaningful provocation to one's own assumptions about an artwork. Moreover, intra‐spective practice fostered a genuine curiosity about others through the sharing of perspectives in ways that suggest such inquiry offers a pedagogic opening by enhancing focus, stimulating curiosity, and mindfully engaging reflective, affective and reflexive dispositions.

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)0.002
metaresearch head score (Gemma)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.236
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.074
GPT teacher head0.343
Teacher spread0.269 · 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 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
Published2022
Admission routes1
Has abstractyes

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