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Record W4405536956 · doi:10.1177/02557614241307698

Trauma-sensitive teaching: Supporting refugee students through music education

2024· article· en· W4405536956 on OpenAlexafffundabout
Kelly Lin

Bibliographic record

VenueInternational Journal of Music Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and experiences of immigrants and refugees
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaMinistère de l’Éducation, Gouvernement de l’OntarioUniversity of Oxford
KeywordsRefugeeMusic educationPsychologyPedagogySociologyVisual artsArtHistory

Abstract

fetched live from OpenAlex

Refugee children are a significant part of contemporary Canadian classrooms. Children who have fled their homes as refugees have experienced trauma and bear the effects of it socially, emotionally and academically. My study identifies trauma-sensitive strategies that music educators in Ontario are using to support refugee students from kindergarten to Grade 8. Through three semi-structured interviews with K-8 music educators, I identified four main themes to trauma-sensitive music instruction. Trauma-sensitive music education (a) provides holistic care for refugee students; (b) supports refugee students in feeling empowered; (c) provides a space in which refugee students can cultivate their sense of personal and collective awareness; and (d) enables refugee students to feel a sense of belonging. The implications of these findings provide music educators with practical tools and strategies to facilitate a learning environment in which refugee students can experience hope and healing.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.140

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.006
Scholarly communication0.0040.002
Open science0.0010.010
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.030
GPT teacher head0.431
Teacher spread0.401 · 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 designObservational
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
Published2024
Admission routes3
Has abstractyes

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