MétaCan
Menu
Back to cohort
Record W4416600906 · doi:10.3390/educsci15121576

From Panopticon to Possibility: Rethinking Music Education Through Biesta’s World-Centered Lens

2025· article· en· W4416600906 on OpenAlexaff
Xiao Dong

Bibliographic record

VenueEducation Sciences · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsWestern University
Fundersnot available
KeywordsPanopticonThrough-the-lens meteringPower (physics)Music educationProcess (computing)Lens (geology)

Abstract

fetched live from OpenAlex

This paper reflects on how the traditional structures of Western classical music education, long reinforced by hierarchical authority and the “expert gaze,” are increasingly unsettled in contemporary practice. Drawing on Foucault’s panopticon as a metaphor, I show how performance-centered, teacher-dominant approaches have disciplined both students and parents while leaving little room for students’ development of subject-ness. Through a real teaching story, I reveal the emerging cracks in this long-standing system, where digital technology, alternative pedagogies, and shifting cultural values have begun to erode the conservatory’s insulated authority. To interpret this change, I draw on Biesta’s three functions of education—qualification, socialization, and subjectification—and his notion of world-centered education. I suggest that music education must not only prepare students with skills and cultural knowledge but also facilitate subjectification—the capacity for agency, responsibility, and freedom. The discussion highlights implications for practice, including teacher judgment, more balanced power relations, and reflective, technology-mediated pedagogies, suggesting that the future of music education lies in creating spaces where learners encounter the world not as passive recipients but as subjects in the process of becoming with it.

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 categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.599
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.164
GPT teacher head0.342
Teacher spread0.179 · 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.

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

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueEducation SciencesSame topicDiverse Music Education InsightsFrench-language works237,207