Exploring preparation for backcountry travel in Bowron Lake Provincial Park, British Columbia
Bibliographic record
Abstract
Outdoor recreation trips have long been conceptualized as relatively linear multiplephased experiences. Previous studies of recreational activities and visitor experiences in backcountry settings have largely focused on the onsite phase. This study, however, explored the nature and elements of preparation and its influence on the backcountry experiences and meanings, as recollected by participants. Secondarily, this project aimed to better understand the roles of technology in preparation. The study used semi-structured interviews with participants who had completed one or more canoe or kayak trips in Bowron Lake Provincial Park in British Columbia, a world-renowned backcountry canoe circuit. Findings indicate that preparation is an ongoing process comprised of multiple elements by which participants are involved throughout all phases of an outdoor experience. Preparation can help improve safety and satisfaction during backcountry experiences and has important implications for recreation and leisure programs and practices, parks and protected areas management, and outdoor education. Conceptually, the research suggests that preparation is not as linear or time-bound as the five-phase model would suggest. Rather, aspects are ongoing throughout and between trips, accrued with mentorship and training over the course of a career, and involve elements of tasks and efforts beyond simply ‘planning’ trip logistics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".