Exploring the Main Determinants of National Park Community Management: Evidence from Bibliometric Analysis
Bibliographic record
Abstract
The establishment of protected areas such as national parks (NPs) is a key policy in response to numerous challenges such as biodiversity loss, overexploitation of natural resources, climate change, and environmental education. Globally, the number and area of NPs have steadily increased over the years, although the management models of NPs vary across different countries and regions. However, the sustainability of NPs necessitates not only effective national policy systems but also the active involvement and support of the local community and indigenous people, presenting a complex, multifaceted challenge. Although the availability of literature on community-based conservation and NPs has increased over the years, there is a lack of research analyzing trends, existing and emerging research themes, and impacts. Hence, in this study, we employed bibliometric methods to conduct a quantitative review of the scientific literature concerning community management of NPs on a global scale. By analyzing data from published articles, we identified research hotspots and trends as well as the quantity, time, and country distribution of relevant research. We developed a framework to illustrate the main research hotspot relationships relevant to NPs and community management, then summarized these findings. Based on the literature from 1989 to 2022, utilizing 2156 research papers from the Web of Science Core Collection database as the data source, visualizations were conducted using the VOSviewer software (1.6.18). Based on the results of network co-occurrence analysis, the initial focus of this field was on aspects of resource conservation. However, with the convergence of interdisciplinary approaches, attention has gradually shifted towards human societal well-being, emphasizing the “social-ecological” system. Furthermore, the current research hotspots in this field mainly revolve around issues such as “natural resources, sustainable development, stakeholder involvement, community management, sustainable tourism, and residents’ livelihoods”. Effectively addressing the interplay of interests among these research hotspot issues has become an urgent topic for current and future research efforts. This exploration necessitates finding an appropriate balance between environmental conservation, economic development, and human welfare to promote the realization of long-term goals for sustainable development in NPs.
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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 | 0.020 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.151 | 0.237 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".