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Children’s Musical Play in a Digital Era

2020· reference-entry· en· W3097027689 on OpenAlexaff
Kari Veblen, Nathan B. Kruse

Bibliographic record

Venuenot available
Typereference-entry
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsWestern University
Fundersnot available
KeywordsMusicalVernacularCitizen journalismSocial mediaSociologyMedia studiesPsychologyVisual artsArtLiteratureComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

This chapter examines the vibrant communities of practice found in online children’s musical play. Many of the children who play, share, and post their musical traditions through social media came into this world after YouTube was introduced in 2005. Children’s clapping games serve as an example of traditional transmission, although this research may not be fully generalizable to other instances of enduring cultural practices or vernacular genres. In this chapter we consider these matters of ethics in online research and the nature of participatory cultures as well as expressive play in childhood. Following this, we survey current research in music learning through YouTube and online children’s clapping games to explore the question of what remains and what changes when such games are shared through social media.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.665
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.021
GPT teacher head0.269
Teacher spread0.247 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2020
Admission routes1
Has abstractyes

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