Participatory Learning in the Early Years
Bibliographic record
Abstract
1. Participatory Learning: Issues for Research and Practice Donna Berthelsen 2. International Perspectives on Participatory Learning: Young Children's Perspectives across Rich and Poor Countries Helen Penn 3. The Guiding Principles of Participation: Infant, Toddler Groups and the United Nations Convention on the Rights of the Child Berenice Nyland 4. 'Doing the Right Thing' - A Moral Concern from the Perspectives of Young Preschool Children Eva Johansson 5. The Desirable Toddler in Preschool -Values Communicated in Teacher and Child Interactions Anette Emilson and Eva Johansson 6. Friendships and Participation among Young Children in a Norwegian Kindergarten Anne Greve 7. Beliefs about Toddler's Learning in Child Care Programs in Australia Jo Brownlee and Donna Berthelsen 8. In Support of a Relationship-Based Approach to Practice with Infants and Toddlers in the United States Mary McMullen and Susan Dixon 9. Looking and Listening For Participatory Practice in an English Day Nursery Paulette Luff 10. Dialogue, Listening and Discernment in Professional Practice with Parents and their Children in an Infant Program: A Canadian Perspective Enid Elliot 11. If You Think They Can Do It - Then They Can: Two-year-olds in Aotearoa New Zealand Kindergartens and Changing Professional Perspectives Judith Duncan 12. Fairness in Participation in Preschool Artin Goncu, Catherine Main and Barbara Abel 13. Contexts, Pedagogy, and Participatory Learning: A Way Forward Jo Brownlee
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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 | 0.065 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.013 | 0.090 |
| Scholarly communication | 0.023 | 0.025 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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".