The Relationship between Parks and Protected Areas and Gateway Communities in the United States and Canada: A Scoping Review
Bibliographic record
Abstract
Individuals engaged in protected area (PPA) management are increasingly interested in the relationships between PPAs and nearby communities. Research suggests that these communities, or gateway communities, share a variety of socioeconomic, environmental, and cultural ties with PPAs. However, these relationships can vary substantially based on contextual factors, including geographic location and national development. Additionally, the absence of a comprehensive literature synthesis on this topic hinders the ability to make meaningful recommendations for policy, practice, and future research. Therefore, the purpose of this scoping review was to: (a) review the existing primary, peer-reviewed research on the environmental, socioeconomic, and cultural relationships between gateway communities and PPAs in the United States and Canada; (b) identify thematic patterns within the reported research; and (c) discuss the resulting implications for scholars and practitioners. This scoping review followed the PRISMA-ScR protocol and yielded 37 peerreviewed research articles that examined at least one relationship between PPAs and gateway communities in the United States and Canada. The majority of studies (n = 21, 57%) were performed in and around national parks, and approximately half employed qualitative methods (n = 18, 49%). Thematic analysis of research topics resulted in 14 themes in five categories, including public participation and collaboration, resident perceptions, community dynamics, policy impacts, and the impacts of PPA establishment. Relationships examined were organized into socioeconomic, environmental, and sociocultural categories. While a few articles examined relationships that fit neatly into one category, most examined relationships in more than one category (n = 30, 81%). Collectively, these studies highlight a range of relationships between PPAs and gateway communities. This synthesis illustrates that even within the same geographic region, relationships may differ. Therefore, the same policies and management actions may not yield the same results in every PPA-gateway community context. This review also reveals patterns, such as the positive effect of public participation and collaborative management on PPA-gateway community relationships and barriers associated with traditional and science-based public engagement strategies. Additionally, this review highlights the potential for theory-based and interdisciplinary research to expand the body of knowledge on this critical topic.
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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) | 0.001 | 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".