“Can We Have More Hand-Drums?” Preschool Children's Musical Play in a Program Exploring Diverse Languages
Bibliographic record
Abstract
This paper explores the role of play in a research project that documented and elucidated responses to a culturally diverse music program of five preschool-aged children in a child-care centre in Melbourne, Australia. The study was conducted over a period of nine weeks. The music program was conducted playfully, concentrating not only on the musical features and premeditated pedagogical devices, but on children's contribution to the content and arrangements of the music sessions. The methodology employed in the study was conceptually rooted in the socio-cultural framework. The researcher took into consideration that learning is purported in a social environment and changes in character within a variety of social contexts. The social aspects of play were well-captured in the large number of Learning Stories that were written about and with the children. The Learning Story method of gathering, analysing and planning from data was employed as this socio-cultural approach encompassed contextual factors and celebrated children's active role in the process of learning within and beyond the music program. The analysis of Learning Stories revealed that children's learning is more profound when there are opportunities for play, on their own terms; this consideration is strongly recommended for future research projects.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".