Children’s invented notations: Extending knowledge of their intuitive musical understandings using a Vygotskian social constructivist view
Bibliographic record
Abstract
Children’s invented musical notations provide a fascinating window into their musical and metacognitive understandings. Using a Vygotskian social constructivist perspective that emphasizes the social nature of cognition, the purpose of this qualitative study was to examine the processes and products of children’s notations as they notated a song and then taught it to a peer, a multi-level task currently unstudied in the literature. Thirteen Canadian children, aged 5–9 years, without prior music instruction, notated a song learned the previous week, sung it back, explained what they did, and then taught the song to a classmate the following week. The children’s notations, as well as audio and videotapes of them carrying out the multi-level task, provided the primary data; audiotaped conversations with parents, teachers, and a school principal provided secondary data. Analyses generated descriptive and narrative portraits, which demonstrate the increasingly sophisticated representational strategies used by the children from three different age groups as they notated, refined, and taught a song. Select portraits are presented and discussed. Implications for research and practice highlight the value of the research task in eliciting children’s multiple intelligences and collaborative problem-solving skills, and painting more complete portraits of their intuitive and emergent musical and metacognitive understandings.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".