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Record W2965856625 · doi:10.36510/learnland.v12i1.988

Culturally Sustaining Pedagogy Through Arts-Based Learning: Preservice Teachers Engage Emergent Bilinguals

2019· article· en· W2965856625 on OpenAlexvenueno aff
Nancy Pauly, Karla V. Kingsley, A. Leroy Baker

Bibliographic record

VenueLEARNing Landscapes · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsnot available
FundersImperial Experimental Cancer Medicine Centre
KeywordsThe artsPedagogyArts in educationModalitiesLanguage artsTeacher preparationPsychologyTeacher educationSociologyMathematics educationVisual artsSocial science

Abstract

fetched live from OpenAlex

Rooted in arts-based learning, funds of knowledge, and culturally sustaining pedagogies, this paper describes the experiences of a cohort of preservice teachers who co-created arts integration units with emergent bilingual students, engaging them in the creation of plays based on culturally relevant children’s literature. This cohort was designed by eight professors to prepare professionals to serve the needs of culturally diverse and economically vulnerable communities through arts-based teaching and assessment modalities. We share three telling cases about these preservice teachers’ reflections on their pedagogy and their students’ engagement illustrating how the arts can foster inclusive ways of knowing and communicating.

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)0.003
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 score0.009
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0090.005
Scholarly communication0.0060.002
Open science0.0010.011
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.038
GPT teacher head0.321
Teacher spread0.284 · 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

Citations14
Published2019
Admission routes1
Has abstractyes

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