Start-Up Games on School Playgrounds: Instances of Ceremonial Rituals
Bibliographic record
Abstract
Abstract This study emerged from naturalistic observations of children’s self-initiated musical play on the playgrounds of 14 Canadian schools, part of a three-year project anchored by the broad research question: What is the nature of children’s spontaneous musical expressions during selfdirected play? Seven of the dozens of play start-up procedures documented during these observations are analyzed, using ritual theories from a variety of scholarly orientations to establish that these start-up games are genuine ritual acts. While some theorists identify ritual in every aspect of quotidian life, the author proposes that children’s start-up procedures are ceremonial rituals, examples of what Dissanayake termed as artifying. They are characterized by an extended and reverent focus on the performance of a series of formalized rhythmic, kinesthetic actions, sustained by a shared belief that the process is meaningful. Some of the possible benefits that these startup rituals provide are considered, recognizing that, as with all play scenarios, there are issues of inclusion and exclusion. The author suggests that respectful examinations of children’s musical play culture contribute to the project of revisioning music education practice by offering insights into the richness and sophistication of children’s musicking proclivities and abilities. The author proposes specific pedagogical applications.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".