The Special Value of Children's Age-Mixed Play.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
From an evolutionary perspective, the normal social play of children involves kids of various ages. Our human and great-ape ancestors most likely lived in small groups with low birth rates, which made play with others of nearly the same age rare. Consequently, the evolutionary functions of children’s social play are best understood by examining play in groups that include children of different ages. The author calls this kind of play “age mixed.” He reviews the research on such play, including his own research conducted at the Sudbury Valley School in Massachusetts where students from ages four to about eighteen mix freely. He concludes that age-mixed play offers opportunities for learning and development not present in play among those close in age, permitting younger children to learn more from older playmates than they could from playing with only their peers. In age-mixed play, the more sophisticated behavior of older children offers role models for younger children, who also typically receive more emotional support from older kids than from those near their own age. Age-mixed play also permits older children to learn by teaching and to practice nurturance and leadership; and they are often inspired by the imagination and creativity of their younger playmates.
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.
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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it