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Record W4411851420 · doi:10.1007/978-3-031-87136-8_3

Building Community Within Online Later-Life Creative Community Music

2025· book-chapter· en· W4411851420 on OpenAlexafffund
Colin Enright, Andrea Creech, Lisa Lorenzino, Mariane Generale

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSense of communityImprovisationActive listeningOnline communityThematic analysisContext (archaeology)Interpersonal communicationIsolation (microbiology)PsychologyCommunity buildingSociologyQualitative researchSocial psychologyCommunicationPublic relationsVisual artsWorld Wide WebComputer sciencePolitical scienceGeographyArt

Abstract

fetched live from OpenAlex

Abstract This chapter focuses on the question of whether and how a strong sense of community and connection can be fostered within an online community music context. The authors draw upon a thematic analysis of interviews carried out with online community music workshop participants and facilitators, focusing on the following questions: 1. Did participants experience a sense of community within the online context, and if so, how was that described? 2. What were the experienced enablers and/or barriers to a sense of community and connection being established? 3. What were the elements that facilitated interpersonal connection, and/or counteracted isolation, within the online creative community music workshops for senior adults? Our analysis revealed pedagogical practices and environmental factors that either helped or inhibited community building. Connection was found to be facilitated through technology, the sense of being in a shared lifeboat, improvisation, meaningful listening, and individual recognition. These are all important factors in designing and planning for future online activities.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.003
Scholarly communication0.0050.004
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.154
GPT teacher head0.393
Teacher spread0.239 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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