Children’s clapping games on the virtual playground
Bibliographic record
Abstract
This study considers children’s informal musicking and online music teaching, learning, playing, and invention through an analysis of children’s clapping games on YouTube. We examined a body of 184 games from 103 separate YouTube postings drawn from North America, Central and South America, Europe, Africa, Asia, Australia, and New Zealand. Selected videos were analyzed according to video characteristics, participant attributes, purpose, and teaching and learning aspects. The results of this investigation indicated that pairs of little girls aged 3 to 12 constituted a majority of the participants in these videos, with other participant subcategories including mixed gender, teen, adult, and intergenerational examples. Seventy-one percent of the videos depicted playing episodes, and 40% were intended for pedagogical purposes; however, several categories overlapped. As of June 1, 2016, nearly 50 million individuals had viewed these YouTube postings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".