Bridging the youth-nature divide through secondary forest schools : insights from Sweden and Canada
Bibliographic record
Abstract
Universal issues of biodiversity loss, climate change, and political divide underscore the need to prepare future generations with the knowledge and skills necessary to address sustainability challenges and foster forest stewardship. This study explores the implementation of place-based education (PBE) principles within forest school programs for youth ages 13–16 across Sweden and Canada. Grounded in critical pedagogy, this research identifies best practices within forest schools, for fostering sense of place, ecological literacy, and student engagement while navigating institutional and political barriers. An ethnographic, comparative case study methodology was utilized. Data were collected through 22 semi-structured interviews with forest school stakeholders and corroborated with 500 pages of pedagogical documentation. The findings demonstrate that forest school programs can serve as powerful vehicles for fostering youth-nature relationships, particularly when they center local contexts, community partnerships, and student agency. While both Swedish and Canadian cases revealed strong alignment with PBE principles, implementation varied significantly based on governance structures, access to forest spaces, educator training, and institutional recognition. Sweden’s centralized education system facilitates national cohesion but can constrain localized adaptation and Sámi knowledge inclusion. Canada’s decentralized model allows for regional flexibility, but often results in unequitable access and under-resourced forest school programming, especially for marginalized communities. Best practices identified across both cases include (1) frequent and consistent engagement with local forest landscapes, (2) interdisciplinary and experiential curriculum design, (3) reciprocal relationships with community and Indigenous knowledge holders, and (4) opportunities for student-driven inquiry. Structural barriers, such as curriculum rigidity and colonial legacies, continue to limit the transformative potential of forest schools. This study contributes to growing discourse on equitable and community-responsive pedagogy, by centering the experiences of secondary students—an age group often overlooked in forest education research. It underscores the need for systems and policy frameworks that are not only ecologically grounded, but culturally sustaining and critically reflexive. By examining how PBE takes shape in diverse socio-political landscapes, this study offers educational guidance for cultivating forest stewardship among youth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".