Singing and Literacy: An Initial Investigation in St. Vincent and the Grenadines
Bibliographic record
Abstract
Music – making music, singing music, listening to music- is a human activity deeply rooted in who we are as people. Blacking (1973) proposed that “Musicmaking is an inherited biological predisposition which is unique to the human species” (p.7). One important focus of music exploration has become “using ethnomusicological insight and approaches in order to understand the character of a music culture- its subcultures, such as children’s music and community music (Dzansi, 2002, ¶ 2). There have been a number of studies of musical play of children in a variety of geographical locations, such as Ghana (Dzansi, 2002), Australia (Marsh, 1995), the United States (Campbell, 1991; 1998), Great Britain (Opie, 1985), and Jamaica (Hopkin, 1984). Even so, Campbell (1998) has suggested that “Children’s engagement in music frequently is paid minimal attention by teachers and parents, even when it may be the rich repository of children’s intimate thoughts and sentiments” (p. 5). In this study, one of the researchers collected “musicking” (Small, 1987) from children and teachers at an elementary school in St. Vincent and the Grenadines. The musical pieces that were recorded included chants, singing games, and hand-clapping songs. In analyzing these pieces, we have investigated the connections between the songs, singing as communication, and children’s literacies. Strong connections may be made between what children sing, and their comfort level in learning to read and communicate. We have found that singing as a literacy is frequently underestimated. The singing of children provides a richer experience of literacies for the schoolchildren of St. Vincent and the Grenadines.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".