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Record W3212335208

Educating from Difference: Black Cultural Art Educators' Perspectives with Culturally Responsive Teaching

2020· article· en· W3212335208 on OpenAlexaboutno aff
Collette Murray

Bibliographic record

VenueYork University Digital Library (York University) · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsnot available
Fundersnot available
KeywordsCultural competenceSociologyPedagogyCultural diversityCultural backgroundHigher educationAestheticsAnthropologyArtPolitical scienceResearch methodology
DOInot available

Abstract

fetched live from OpenAlex

The 2009 Ontario Ministry of Education's Equity Action plan called for school boards to implement culturally relevant teaching in their strategic plans.As senior administration and educators work towards inclusive classrooms, a perspective that remains absent is that of the arts educator and their relationship to culturally responsive pedagogy.This qualitative study uses Critical Race Theory to examine the work and experiencesincluding the successes and challenges-of Cultural Art Educators using African diasporic artforms.The narratives from semi-structured interviews with eight Black Canadian artists, uncover that while successes occur, cultural art educators navigate the politics of institutional unpreparedness, Anti-Black racism, delegitimization of their cultural artistry and cultural appropriation.Institutional recommendations are made to understand the artists' role, improve the working relationship and recognize Black art content supporting a Canadian education mandate.This is a valuable contribution to the topic of cultural relevance that counters the historical exclusion of race-based data of artists involved in education.

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)0.020
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.334
Threshold uncertainty score0.664

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0700.042
Scholarly communication0.0220.009
Open science0.0030.016
Research integrity0.0040.013
Insufficient payload (model declined to judge)0.0050.000

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 head0.017
GPT teacher head0.177
Teacher spread0.160 · 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 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

Citations0
Published2020
Admission routes1
Has abstractyes

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