Bridging Research and Practice:Young Children’s Agency and Resilience in a Climate Changed World
Bibliographic record
Abstract
The World Forum on Early Care and Education conference is held every two years and focuses on issues related to young children, their care and education. The program for the 2024 event in Vancouver, Canada, included a theme about the impact that climate change is having on young children and their families. The invitation to present on this theme was accepted by a number of practitioners, researchers and policy makers from a range of countries and resulted in several sessions throughout the four days of the conference, discussing and presenting research, practice and pedagogy for supporting young children and their families to adapt to and develop resilience in the face of climate change. The coming together of so many early childhood professionals with an interest in this topic also resulted in the inauguration of an International Working Group on Young Children’s Resilience and Agency in the face of climate change. This paper reports on one of the conference sessions: #62 where early childhood educators, researchers, and managers came together to hear the presentations and to share their own perspectives in the discussion that followed.
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 | 0.048 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.022 | 0.044 |
| Scholarly communication | 0.026 | 0.015 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".