Bibliographic record
Abstract
In his 2018 study How to Make Children Laugh, Michael Rosen writes that when he performs silly songs and poems in schools, “I become the channel through which the children can, for a moment, let go of their anxieties about the authority figures in their lives” (23). The laughter mediates between the norms of adulthood and the anxieties of childhood. Although silly songs are perennially popular with children and their caregivers alike, they remain understudied. This article, then, asks how laughter works in the context of English-language silly songs for preschool and early elementary children. Drawing from Rosen’s theorization, it combines the perspectives of literary studies and early childhood music education to analyze well-known silly songs recorded by Sharon, Lois & Bram, Raffi, and others. Grown-up performers invite laughter by acting like goofy big kids who can play along with the children and by inverting the authority relationship between children and adults. Silly songs create deliberately incongruous, cheekily subversive experiences that can help children’s anxieties to be released and rendered nonthreatening.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".