Managing Recreational Experiences in Arctic National Parks: A Process for Identifying Indicators
Bibliographic record
Abstract
Despite low use densities and a largely absent development footprint, parks in arctic environments are confronted with questions similar to more heavily used protected areas. Many of these questions concern the character of experiences visitors seek and for which agencies attempt to provide opportunities. These experiences, like others, have a variety of dimensions, such as solitude, adventure, naturalness, scenery, and so on. Understanding these experiences and ensuring that visitors have an opportunity to experience them are major challenges for stewardship organizations, given the character and remoteness of the setting. This paper describes a three-phase project to discover the dimensionality of experiences among visitors to Canada?s Auyuittuq National Park and develop indicators that managers could use to assess if such desired experiences were being achieved. In Phase I, the project used qualitative interviews to identify the dimensionality of experiences and in Phase II quantitative methods to assess their importance to visitors as well as to link experiences to various setting attributes. Phase III involved a workshop involving managers, scientists, and tourism officials to identify potential indicators of each desired dimension of the visitor experience. The process used here ensured that research was policy relevant and may serve as a model for other park and protected area stewards faced with similar challenges.
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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 | 0.034 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.008 |
| 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".