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“It’s All about Confidence and How You Perceive Yourself”: Musical Perceptions of Older Adults Involved in an Intergenerational Singing Program

2014· article· en· W2510741711 on OpenAlexaffabout
Jennifer Hutchison, Carol Beynon

Bibliographic record

VenueLiteracy Information and Computer Education Journal · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsWestern University
Fundersnot available
KeywordsSingingPerceptionMusicalPsychologyAudiologyVisual artsArtMedicineAcousticsNeuroscience

Abstract

fetched live from OpenAlex

This study explores and identifies the perceptions of older adults involved in an intergenerational singing curriculum that brought a group of 20 seniors together with a Grade 2 class of children in one small Ontario community. During six, one-hour sessions, the seniors and children experienced a carefully-designed curriculum that explored songs across various themes that served as a vehicle in fostering dialogue, musical discourse and shared learning among the participants. At the conclusion of the program, interviews were conducted, and seniors, children, and administrators were encouraged to share their personal musical narratives and background, in addition to their experiences from and about this program. There is little research that details the benefits of intergenerational singing, however observational and narrative data from this study, revealed a prevalent theme pertaining to the seniors' perception of lack of confidence, concerns about perceptions of themselves by the children, and musical inadequacy. In an effort to enhance singing education programs for every generation, these findings may provide the groundwork for social and musical considerations as program designers and leaders establish intergenerational music programs that facilitate optimal music engagement across the generations.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.003
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.002
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 head0.018
GPT teacher head0.271
Teacher spread0.253 · 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 designObservational
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

Citations2
Published2014
Admission routes2
Has abstractyes

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