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Constructions and Negotiations of Identity in Children’s Music in Canada

2012· book-chapter· en· W282129717 on OpenAlexaffabout
Anna Hoefnagels, Kristin Harris Walsh

Bibliographic record

VenueOxford University Press eBooks · 2012
Typebook-chapter
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsMemorial University of NewfoundlandCarleton University
Fundersnot available
KeywordsRepertoireMusicalNegotiationSingingIdentity (music)Context (archaeology)Visual artsPopular musicLiteratureSelection (genetic algorithm)ArtAestheticsSociologyHistorySocial scienceArchaeologyComputer scienceAcoustics

Abstract

fetched live from OpenAlex

Abstract This article reviews research and the historical context for Anglo-Canadian children’s folk music, the development of the children’s folk “movement” in the 1970s, and the resultant Anglo-Canadian children’s musical canon. Specifically, it explores the relationship between musicians and the selection and contents of songs through the repertoire of two musical “acts”: the threesome known as Sharon, Lois, and Bram; and Raffi Cavoukian. The analysis and comparison of the music, performance practices, and songs of both acts show that despite their varied approaches, each drew upon folk and popular musical traditions, and created new songs in forming their repertoires. Both acts created music that empowered children as the keepers of the future through their use children’s artwork, children’s voices, and efforts to teach children how to care of themselves and the world around them.

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)0.002
metaresearch head score (Gemma)0.003
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: none
Teacher disagreement score0.147
Threshold uncertainty score0.989

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0340.029
Scholarly communication0.0120.002
Open science0.0020.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.000

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 head0.031
GPT teacher head0.181
Teacher spread0.150 · 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
Published2012
Admission routes2
Has abstractyes

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