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Record W2910477430 · doi:10.1177/0255761418821165

Exploring post-degree employment of recent music alumni

2019· article· en· W2910477430 on OpenAlexaff
Julia Brook, Sue Fostaty Young

Bibliographic record

VenueInternational Journal of Music Education · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsQueen's University
Fundersnot available
KeywordsVariety (cybernetics)Music educationCurriculumPerceptionWork (physics)MusicalField (mathematics)PsychologyPublic relationsPedagogySociologyPolitical scienceVisual artsComputer scienceEngineering

Abstract

fetched live from OpenAlex

The purpose of this research was to identify the various types of employment held by music alumni at one university. We also compare the perceptions of alumni who currently work primarily in music with those of alumni who work outside the field. A mixed-methods research design that relied on surveys and interviews was used to gather data. Alumni employed primarily outside the field held a wide variety of roles and many reported incorporating their musical skills in these roles and they continued to engage in a variety of activities within the field of music. Those whose employment was primarily in music were more likely to have reported choosing to enroll in a music degree program with an express intention of gaining music-focused employment. Findings from this study illustrate that while graduates of music programs do find employment success, further investigation is necessary to identify the breadth of roles available, yet untapped, for music graduates and the perhaps entrepreneurial requirements for engagement in them. Our findings also point to the need for music programs to realign curriculum to better reflect the ever-expanding music sector.

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 categoriesInsufficient 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 score0.846
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0220.000

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 head0.265
GPT teacher head0.303
Teacher spread0.037 · 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

Citations15
Published2019
Admission routes1
Has abstractyes

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