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Record W4386845959 · doi:10.29173/inton85

Live-streamed performance & intercultural education

2023· article· en· W4386845959 on OpenAlexvenueno aff
Felicia K. Youngblood, Ramin Yazdanpanah, David Cobb, Silviu Ciulei

Bibliographic record

VenueIntonations · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsnot available
Fundersnot available
KeywordsExperiential learningGeneral partnershipPedagogyIntercultural learningPresentation (obstetrics)Music educationPsychologySpace (punctuation)MultimediaSociologyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

This article provides creative solutions for online world music pedagogy that were developed in response to the widespread need for digital education during the Covid-19 pandemic. Innovations in the synchronous presentation of online music courses and performances influenced the authors to collaborate on ways to create space for interaction between students and musicians who share their music and experiences as cultural experts. This approach led to the development of the World Music Guest Artist Series, which allows for experiential learning and intercultural exchange through live-streamed performances and interviews with musicians in various global genres and locations. In mapping out the process, experiences, and benefits of our partnership for educators, students, and musicians, we ultimately intend to showcase our model to higher music educators that endeavor to foster experiential learning and intercultural dialogue in their classrooms through collaboration with cultural mediators, whether in a virtual or in-person learning environment.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.049
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0050.004
Open science0.0010.009
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0490.006

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 head0.283
Teacher spread0.199 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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