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The role of the arts in professional education: Surveying the field

2015· article· en· W1948756594 on OpenAlexaff
Christine Jarvis, Patricia Gouthro

Bibliographic record

VenueStudies in the Education of Adults · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsMount Saint Vincent University
Fundersnot available
KeywordsThe artsProfessional developmentSociologyArts in educationPedagogyEngineering ethicsProfessional studiesPerspective (graphical)EmpathyPsychologyPolitical scienceEngineeringSocial psychologyVisual arts

Abstract

fetched live from OpenAlex

Many educators of professionals use arts-based approaches, but often explore this within the confines of their own professional disciplines. This paper consists of a thematic review of the literature on arts and professional education, which cuts across professional disciplines in an attempt to identify the specific contribution the arts can make to professional education. The review identified five broad approaches to the use of the arts in professional education: exploring their role in professional practice, illustrating professional issues and dilemmas, developing empathy and insight, exploring professional identities and developing self-awareness and interpersonal expression. Woven through these approaches we found that the development of a more sophisticated epistemology and a critical social perspective were common outcomes of art-based work in professional education. Arts-based approaches may help learners to make a critical assessment of their own roles and identities within professions, and to consider the impact of professions in shaping the broader society.

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)0.012
metaresearch head score (Gemma)0.028
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 score0.012
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.005
Science and technology studies0.0030.004
Scholarly communication0.0060.006
Open science0.0010.003
Research integrity0.0010.001
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 head0.083
GPT teacher head0.345
Teacher spread0.262 · 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

Citations24
Published2015
Admission routes1
Has abstractyes

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