Cultivating Nature Connection: Instructor Narratives of Urban Outdoor Education
Bibliographic record
Abstract
Background: Outdoor education often aims to facilitate positive human–nature relationships and craft healthy, sustainable lifestyles. Processes and outcomes of program innovations seeking to address “nature-deficit disorder” among children can be understood from a narrative perspective. Purpose: This study illuminates how a group of instructors working for a charity-based outdoor organization in Toronto, Ontario, perceive the cultivation of nature connectedness in and through the urban outdoor education programs they facilitate for children. Methodology/Approach: A narrative methodology was used to engage instructors in telling personal stories about their involvement and perceptions of programs they facilitate, and to interpret thematic insights into the broader meanings circulating within this instructor group. Findings/Conclusions: Analyses revealed that instructors story the cultivation of nature connectedness around three spatial metaphors: creating space for nature connection, engaging that space, and broadening that space. Findings cast light on how instructors situate their practices within a broader community committed to mentoring nature connectedness in individuals, families, and society. Implications: Instructor stories shed light on contemporary practices of outdoor experiential education, and the meanings and perceived impacts of nature-based learning. The study contributes to literature illustrating the promise urban outdoor education holds for fostering nature connectedness.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".