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Record W2737020452

Critical Pedagogy for Music Education: Preparing Future Teachers

2017· article· en· W2737020452 on OpenAlexaff
Kelly Bylica

Bibliographic record

VenueScholarship@Western (Western University) · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsWestern University
Fundersnot available
KeywordsPedagogyReflexivityCritical thinkingCritical pedagogyTeacher educationMathematics educationMusic educationPsychologySociology
DOInot available

Abstract

fetched live from OpenAlex

This introductory workshop will focus on using critical questioning techniques to encourage education students to think through different lenses when approaching education methods and pedagogy. Undergraduate education students, particularly music education students, often enter university with a desire to replicate an educational experience, program, or teacher they had as an elementary or secondary school student. The use of critical questioning will help students think outside this narrow box of experience and create space to consider in what ways we may be complicit in perpetuating norms that may not be inclusive of all students. Research indicates that the use of critical questioning promotes student reflexivity and engages students in higher level thinking (Abramo, 2015; Pagliaro, 2011; Yang, Newby, & Bill, 2005). In addition, this practice is important as education students and pre-service teachers with experience in critical questioning will have the tools necessary to promote critical thought and questioning in their own classrooms as educators. Although this content is specific to music education students, applications to educators in all subjects will also be acknowledged and facilitated in this workshop.

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)0.010
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0070.004
Scholarly communication0.0070.006
Open science0.0020.007
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0110.003

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 head0.145
GPT teacher head0.439
Teacher spread0.294 · 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 designTheoretical or conceptual
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".

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Citations1
Published2017
Admission routes1
Has abstractyes

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