Bibliographic record
Abstract
Due to Covid-19, much time and effort are dedicated to sanitizing at home and school. While we strive to protect children, we must re-examine the messages they receive about dirt and muddy play. Are we inadvertently prejudicing them against “unclean” entanglements that may afford them more significant learning opportunities? In this paper, I offer my experiences exploring the language of mud and its relationship with play, language, and learning for my four-year-old son. I explore how a mud kitchen can offer caregivers and children a space for collective inquiry in a post-pandemic world. This paper suggests that caregivers’ attitudes towards mess impact children’s openness to muddy play. Also, it highlights that muddy play can be a learning tool to encourage self-expression and teach personal hygiene and cleanliness. I discovered that storytelling normalizing muddy play positively impacted levels of engagement in the mud kitchen. I believe my findings demonstrate the value of mud for developing children’s resourcefulness, curiosity, responsibility, empathy, and self-reliance. My findings emphasize that children can thrive within discomfort with strategic support and compassion from caregivers. I hope that my experiences of muddy play can invite educators to reimagine educational engagements for the future.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.029 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".