Community-based Early Childhood Environmental Education: Narratives of Forest Explorations between Costa Rica and Canada
Bibliographic record
Abstract
This study shares a pedagogical inquiry into Early Childhood Environmental Education (ECEE). With praxis in mind, I connected the academic theory which I was learning with fieldwork practice, aiming to explore more critical understandings of ECEE and share them alongside the growing conversations and stories engaging seriously with young children and their environments. What resulted was an exploration, a ‘first-step’ for myself and participants, towards learning how to build an ECEE project based on participant and community interests. As such, processes and protocols were fluid, as participants and myself navigated and experimented with individual and group learning interests, capacity-building, and teaching/learning with young children about/in/for the ‘natural world’. Exploring learning possibilities through facilitating an inquiry-based community action project focused on ECEE, I asked: (1) How might a group of Toronto daycare students, their families, teachers, interested members in the Las Nubes community, and myself (a FES researcher), collaboratively work together to engage with, learn about, and reflect on our local ‘natural worlds’ in dynamic, collaboratively-border crossing, ways? and (2) What co–constructed experiential narratives might be ‘storied’ as pedagogical lessons of engaging with ECEE? How might the outcomes from the project impact others? As the project emerged, participants engaged in exploring the pedagogical opportunities of group forest walks with children through collaboratively experiencing and sharing their different ways of understanding our local world(s) through observation, documentation, and arts-based methods. While finding shared migratory species was the initial interest, what developed was a collaborative project connecting and sharing the situated learning experiences and understandings of conducting group forest walks and related ECEE activities from each site, aiming to encourage further forest explorations with young children. The study does this by providing a narrative inquiry focused on sharing co-constructed stories and knowledge which grew out of the project. Major narrative themes which emerged were: \nnavigating systemic barriers of/through ECEE practice; ECEE collaboration with/between all ages and experience levels; navigating ethics in practice, safety/risk in ECEE, Stand-out ECEE activities, and children’s expressed EE interests.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.027 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".