#Playrevolution: Engaging Equity through the Power of Play
Bibliographic record
Abstract
This special issue continues a two-year conversation about a #playrevolution in literacies research, theory, and practice. The juxtaposition of play and revolution is intentional, highlighting the tension between play's prosocial benefits and collaborative production and the rapid change, uncertainty, and violence in today's schools, where we desperately need more humanizing elements that build people's connections to one another. The #playrevolution calls educators and researchers to explore the (un)predictable, (un)expected knots emerging through the coalescence of play and literacies, while also considering the possibilities play holds for educational equity in contemporary times. Bringing together twelve educational researchers across the United States, Canada, and Australia, this #playrevolution special issue explores the lively ecology of play-literacies in a variety of spaces—traditional writing and storytelling workshops, digital dialogues, video games, teacher-education courses, makerspaces, and playgrounds—with learners from preschools and kindergartens to high schools and universities.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".