Synergies of affordances and place-based relationality in Forest School practice: implications for socio-emotional well-being
Bibliographic record
Abstract
Research shows that the human-nature relationship positively impacts human well-being. Forest School (FS) practice offers young children a structured program of nature connection through activities, aiming to enhance their self-esteem and social skills. FS is now adapted in countries such as Australia, Canada and New Zealand where a unique cultural interface occurs between European settlers and Indigenous peoples. Responding to socio-cultural diversities, geographical contexts, and the traditional ecological knowledges, FS needs to go beyond play pedagogy and incorporate theoretical perspectives that promote human-nature relationship in local context-specific environments. We argue that the synergies between Western perspectives on affordances perceived in person-environment relationship and Indigenous place-based relationality perspective provide a more suitable approach for developing reciprocal relationships between FS participants and land/place/nature. We propose that the synergies between affordances perceived in FS and place-based relationality cultivated in participants will enhance social and emotional well-being. We call for specific research investigating such synergies supporting participant well-being. Future research on FS practice should be directed toward initiating and exploring co-designed studies by Indigenous and non-Indigenous researchers incorporating methodologies that study participant experience as well as evaluating the impact of FS programs embedding affordances and place-based relationality perspectives.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".