We Are Wilderness Explorers: A Review of Outdoor Education in Canada
Bibliographic record
Abstract
Background: Outdoor education (OE) should be understood in place, time, and culture because it is not a universal teaching approach. We currently know little about what constitutes the “Canadian ways” of doing OE or what students gain from the experience. Purpose: Our goal was to (a) identify the underlying factors guiding OE programs in Canada and (b) describe the learning outcomes and psychosocial benefits of engaging in OE from the students’ perspective. Methodology/Approach: We conducted a systematic review of qualitative studies that examined OE in Canada. We searched published studies from electronic databases (1980-2018). We used meta-ethnography to synthesize the findings. Findings/Conclusions: We reviewed 21 studies reporting on the experiences of 508 students. Using thematic analysis, we identified eight themes highlighting process, goals, and learning outcomes. We developed a model that describes the common teaching components, learning process, and short-term outcomes for OE in Canada. Implications: Our results represent the first study to synthesize OE published research in Canada, which help form a unified voice and a distinctive national identity for OE in Canada. Our results serve as a catalyst for educators to share ideas, practices, and learning goals.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.022 | 0.036 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".