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Record W2325122250 · doi:10.14288/1.0054968

An ethnography of secondary school student composition in music : a study of personal involvement within the compositional process

2009· article· en· W2325122250 on OpenAlexaboutno aff
Alex Tsisserev

Bibliographic record

VenuecIRcle (University of British Columbia) · 2009
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsnot available
Fundersnot available
KeywordsEthnographyComposition (language)Process (computing)SociologyPedagogyPsychologyMathematics educationLinguisticsComputer scienceAnthropologyPhilosophy

Abstract

fetched live from OpenAlex

This study deals primarily with the way secondaryschool students use music to express their personal, inner feelings through composition. Because the topic of personal expression through music composition is an elusive one, my methodology borrows and combines analytical tools from the fields of phenomenology, hermeneutics, semiotics, ethnography and English language arts, and incorporates these tools in the exploration of student's processes, of self-expression through music composition. For a period of several weeks I worked with a sixteen-student sample group,in a Vancouver high school. Each of the students handed in a musical composition. Four of the students granted me interviews in which they shared their views of their compositional processes and the resulting musical works. By the conclusion of the study, the students had displayed the ability to communicate certain ideas, images and emotions, and express themselves by articulating their own unique sense of being through their musical compositions. Furthermore, the students demonstrated a level of musical awareness which has very little to do with the type of proficiency-based music learning that is prevalent in many of today's music education classrooms. Most importantly, however, this study spawns a methodology which examines students' compositional processes rather than their finished musical products.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.591
Threshold uncertainty score0.972

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)0.0010.000

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 head0.027
GPT teacher head0.224
Teacher spread0.197 · 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 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

Citations5
Published2009
Admission routes1
Has abstractyes

Explore more

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