“Give Us a Privacy”: Play and Social Literacy in Young Children
Bibliographic record
Abstract
This study argues that children require social literacy to protect and sustain their play: In play, they construct an emotional landscape of “we”-ness to be protected from outsiders. The research consisted of a 4-month-long investigation, which involved audiotaping and videotaping children engaged in self-initiated play in a kindergarten class at a college lab setting. Data analysis demonstrated that sustaining play required the co-construction and development of social literacies that allowed children to manage play props and roles, support emotional well-being among participants, facilitate collaboration among participants, and prevent or resolve conflicts, either among participants or between participants and intruders. In addition, generating rules to protect the boundaries of play, employing verbal and nonverbal forms of resistance, and avoiding contact with intruders assisted children in protecting their play. This article hopes to bring to the daily work of early childhood educators the awareness that not only is play a socially complex and dynamic context that provides children with opportunities to develop social competence, but that children's creation, expansion, and protection of their social relations—that intimate “we” inside the play—require support and respect from educators. Children attempt to protect and sustain their play, and early childhood educators offer support if they can “read” and appreciate children's social literacy in play.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".