Managing Recreation in New York's Adirondack Park: A Case Study of Public Perceptions and Preferences for Reducing User Impacts to the High Peaks Wilderness Complex
Bibliographic record
Abstract
This qualitative case study research investigated public perceptions and preferences regarding management options for addressing recreational impacts to the High Peaks Wilderness Complex (HPWC) in New York State’s six-million-acre Adirondack Park. The Park is the largest in the contiguous United States, attracting local and international visitors from Philadelphia, Montreal, Boston, and New York City, major cities within 350 miles of the HPWC. The Park saw 12.4 million visitors in 2018, resulting in crowding, trail erosion, clandestine trails/campsites, water pollution, and plant/wildlife impacts. Data was gathered from 1,200 individuals via an online questionnaire, semi-structured interviews with NGOs, community influentials, and agency representatives, and participant observation. Findings showed the public strongly supported passive management options such as increased funding for education, trail reconstruction, enhanced management of the HPWC, and the Department of Environmental Conservation (DEC) promotion of alternatives to the most popular wilderness trails during busy months. The public was split in their support of direct management techniques such as temporary trail closures, limiting the number of hikers, and mandatory permits for hikers/parking. NGOs expressed a diversity of preferences for direct wilderness management, but widely supported enhanced education, trail improvements, and funding increases for management and the hiring of more rangers. The results of this research provide insights for improving management practices that facilitate sustainable recreation while also protecting and restoring federal and state designated wilderness. This manuscript culminates in a suite of management implications based on our research findings, including filling all vacancies within the Adirondack Park Agency Board with professionally and culturally diverse individuals, including women, Tribal representatives, minority communities, environmental attorneys, natural scientists, and regional planners. Funding should be allocated for the hiring of additional rangers, Summit Stewards, and trail crews, for enhanced trail maintenance and hiker education efforts. We also recommend implementing the Wildland Monitoring Program in order to better understand trail carrying capacity and ecological limits. Limiting the number of hikers/vehicles through a permit system is but one solution if efforts to heighten ranger presence, education, and improve trails all fail to address resource degradation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".