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Record W2886510677 · doi:10.22176/act17.2.50

Control, Constraint, Convergence: Examining Our Roles as Generalist Teacher Music Educators

2018· article· en· W2886510677 on OpenAlexaffabout
Danielle Sirek, Terry Sefton

Bibliographic record

VenueAction Criticism and Theory for Music Education · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsGeneralist and specialist speciesConstraint (computer-aided design)Convergence (economics)Mathematics educationPsychologyComputer sciencePedagogyMathematicsBiologyEcologyEconomics

Abstract

fetched live from OpenAlex

This research explores the effects of institutional constraints on instructional practices in a preservice generalist teacher music education program in Ontario, Canada.Using Institutional Ethnography and document analysis of active texts, we, an adjunct and tenured professor, use our own experiences to elucidate the multiple points of control and constraint in which teacher education instructors operate.We examine the ways in which "official" documents, such as course outlines, activate institutional expectations and relations of power, and promote standardization (convergence).We explore factors that influence our curricular choices, pedagogical strategies, and occasional acts of resistance; and how these impact differently tenured and adjunct faculty.The paper includes an introduction to the Action Research project that sparked this inquiry, in which we are investigating generalist teacher confidence and engagement with teaching music in the elementary classroom.

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)0.013
metaresearch head score (Gemma)0.026
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 score0.466
Threshold uncertainty score0.926

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.026
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0230.052
Scholarly communication0.0130.005
Open science0.0030.012
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.084
GPT teacher head0.311
Teacher spread0.227 · 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

Citations3
Published2018
Admission routes2
Has abstractyes

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