Elucidating social-ecological perceptions of a protected area system in Interior Alaska: a fuzzy cognitive mapping approach
Bibliographic record
Abstract
The Interior of Alaska is one of the few remaining places in the world with intact ecosystems. Protected areas in this region, particularly Denali National Park and Preserve and Denali State Park, are high-profile tourism destinations situated in a rural landscape that is inhabited by a diverse array of stakeholders. Public land management agencies are faced with the challenging task of engaging these rural residents in discussions about their relationships with a rapidly changing landscape to understand change and growth. This study evaluated residents’ perceptions of social and ecological dynamics of protected areas in Interior Alaska using data from fuzzy cognitive mapping exercises that were part of focus groups and interviews across six local communities. Guided by an exploratory resilience framework, we established a baseline understanding of features that characterized social and ecological conditions at a regional scale. Results showed how residents valued a variety of socio-cultural, socioeconomic, and ecological features of the landscape. The region was predominantly characterized by tourism, sense of community, subsistence, and wilderness. Climate change and large-scale development were the primary drivers of change. Our findings also showed that although the characterization of the region was shared in many ways, there were nuanced differences articulated by residents in each community that warrant attention. These findings provide a structured platform for building resilience and interpreting variability in visions for the future.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".