Beresin, A., & Bishop, J. (Eds.). (2023). Play in a COVID frame: Everyday pandemic creativity in a time of isolation. Open Book Publishers.
Bibliographic record
Abstract
Anna Beresin and Julia Bishop, editors of Play in a COVID Frame: Everyday Pandemic Creativity in a Time of Isolation, embark upon the ambitious task to explore how children, adolescents, and adults engaged in various forms of play during various stages of the COVID-19 pandemic.As chapters include scholars across a multitude of disciplines (including: folklore, anthropology, education, psychology, sociology, art history, communication, and cultural studies) navigating diverse geographic spaces (including: Australia, Canada, England, Finland, Ireland, Japan, Scotland, Serbia, Sudan, South Korea, the United States, and Wales), the book encompasses a wide breadth of play-related inquiries, generating a rich depth of knowledge.The book is certainly a mosaic as authors document their diverse methodological approaches, ranging from traditional methods (e.g., interviews) to visual data generation methods (e.g., photographic essays).Further, the representation of perspectives and experiences of children and adolescents, education workers, play workers, community health advocates, project managers, among other voices, broaden and enrich the variety of discussions.Consequently, the strength of the text lies in the diversity of inquiry focuses, perspectives, methods, disciplines, and geographic spaces shared throughout the chapters.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.014 |
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".