Remembering why forest schools are important: Nurturing environmental consciousness in the early years
Bibliographic record
Abstract
This thesis used a narrative inquiry approach to examine the memories that led certain individuals to support the forest school movement in Canada. Forest schools, a form of outdoor alternative education, have been gaining popularity in North America and Europe in recent years. The first forest school in Canada opened in 2008. By 2014, there were 12 Canadian forest schools serving children in the preschool to kindergarten age group. Who are forest school supporters? What first motivated them to become interested in forest schools? Were these individuals raised in rural areas, or were they urban dwellers who were intrigued by the forest school concept? What significant events in their lives caused them to be drawn to forest schools? What has made forest schools so special to them? These research questions are answered by examining the memories of forest school supporters. Memories influence personality, knowledge and identity. The theoretical framework for this thesis is based on the notion that the forest school movement is motivated by the ecological consciousness of its supporters, which is derived from memories of experiences in nature. Ecological consciousness represents an active, memorable relationship with nature. The author examined his own personal memories and those of five forest school supporters, using an autobiographical approach (currere) and participant interviews. Narrative writing formed the first step of the analysis. The collected memories were written into chronologically-arranged stories that captured important aspects of each person’s life and significant experiences in their formative years. The detailed memories in the narratives were then coded, categorized and themes were extracted. Major findings were that support for forest schools was largely influenced by formative and memorable experiences in nature. Cultural background, opportunities for independent learning, the exhilaration of childhood exploration, and finding refuge in nature were the themes that most influenced ecological consciousness and later forest school support. This study raises fundamental questions about the role of outdoor education in early childhood and its effects on developing the ecological consciousness of future generations.
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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 | 0.002 | 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 | 0.011 | 0.015 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".