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Record W2980251112 · doi:10.1007/978-3-030-21029-8_9

To Honor and Inform: Addressing Cultural Humility in Intercultural Music Teacher Education in Canada

2019· book-chapter· en· W2980251112 on OpenAlexafffundabout
Lori-Anne Dolloff

Bibliographic record

VenueLandscapes: the arts, aesthetics, and education · 2019
Typebook-chapter
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsCultural humilityHumilityIndigenousPedagogySociologyCurriculumMulticulturalismCultural competencePolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract In this chapter I address the need for reshaping the way we think about Indigenous inclusion in the intercultural curriculum. While Canada has prided itself on its multicultural heritage, the nation’s relationship with the First Peoples – First Nations, Inuit and Métis – has been immoral, genocidal and assimilationist. The 2015 publication of the findings of the Truth and Reconciliation Commission’s Calls to Action details a way forward from the colonial patriarchy of the past. Education in general, and music education in particular are charged with finding ways to incorporate Indigenous knowledge, perspectives and history into the curriculum. After an introduction to the issues of the marginalization of Indigenous voices I discuss several arts-based curricular and extra-curricular initiatives that reframe intercultural music education. I propose that developing cultural humility in music teacher education will be a step forward in the decolonization of our teaching and learning spaces. This includes a move from generic cultural competencies toward an attitude of ‘Cultural Humility’. Cultural Humility is discussed as an attitude toward engagement with peoples of cultures other than one’s own. The stance is based on life-long, self-reflective inquiry, and seeks to disrupt the power imbalance that defines ‘othering’, seeking to establish partnerships and collaboration.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.690
Threshold uncertainty score0.708

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.039
GPT teacher head0.311
Teacher spread0.272 · 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.

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

Citations19
Published2019
Admission routes3
Has abstractyes

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