Musical and Textual Content in Childrens Vocalizations
Bibliographic record
Abstract
Children around the world interact with music. They sing, chant, dance, and clap as part of their daily lives. From the shortest musical utterances, to full songs, singing is present wherever children are found. The myriad ways of learning to sing informally go beyond the direct influences and efforts of teachers and other adults, and involve vocalizations across a spectrum of genres that include singing/songs, chanting/chants and musical babble/utterances. In this lecture-demonstration, a comparison of children’s informal and formal singing culture will be explored. While there is extensive research regarding teaching children to sing, and the developing child’s voice as it relates to school and ensemble music, the field of children’s informal singing has been slowly developing. A comprehensive review of the relevant scholarship from within the disciplines of music education, music pedagogy, ethnomusicology, communications, folklore (as well as anthropology, sociology, and psychology) in the field of children’s informal singing behaviors with attention to the nature of their songs, singing engagements, and the process of song acquisition and transmission will be discussed. Particular focus will be given to cross-cultural examination of children’s interactions with singing as it presents in several different countries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".