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Record W3153229061 · doi:10.2458/jcrae.4866

Educating Diversely: The Artist Talk Platform

2018· article· en· W3153229061 on OpenAlexaffabout
Arianna Garcia-Fialdini

Bibliographic record

VenueJournal of Cultural Research in Art Education · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsConcordia University
Fundersnot available
KeywordsRefugeeSociologyImmigrationSpace (punctuation)Visual artsSocial justiceMedia studiesPedagogyPublic relationsPolitical scienceArtSocial scienceComputer scienceLaw

Abstract

fetched live from OpenAlex

Artist-teachers can inspire diverse audiences to adopt innovative teaching practices through artist talks fueled by a social justice and awareness-raising agenda. This article presents ways to incorporate social problematics like mass displacement and pressing international immigration policies into diverse art classrooms and unconventional pedagogical platforms (in this case, through artist talks). It focuses on an artist talk given in collaboration with the Immigrant Workers Centre, a non-profit organization of newly arrived immigrants and refugees located in Montreal, Canada, and explores how the artist-talk platform reaches communities outside the traditional classroom and creates space for an exchange of ideas, artistic intervention, and learning from diverse participants. Additionally, I discuss my observations on potential pedagogical exchanges based on experiences from the event, concluding by further exploring the relevance and potential development in personal artistic and teaching practices for and with this specific community.

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)0.005
metaresearch head score (Gemma)0.006
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: none
Teacher disagreement score0.025
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0140.007
Scholarly communication0.0120.005
Open science0.0020.018
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0250.004

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 head0.197
GPT teacher head0.423
Teacher spread0.227 · 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
Published2018
Admission routes2
Has abstractyes

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