The Perception of Stakeholders on the Forest Ecosystem Services: National Parks in China and Canada
Bibliographic record
Abstract
The stakeholders’ perceptions of forest ecosystem services (FESs) vary among different stakeholder groups. This study, using China’s Shennongjia National Park Pilot (SNP) and Canada’s Banff National Park (BNP) as case studies, identifies and evaluates the preference characteristics of key stakeholders (including farmers, individual operators, government officials, and tourists) toward various FESs. We utilized Q-methodology and semistructured interviews to conduct a sorting of 23 Q-statements regarding FESs, across 7 categories (ranging from −3 to +3), with 24 Q-participants. Stakeholders’ preferences toward FESs were categorized into 3 common perspectives: tourism and culture, production and livelihood, and ecological conservation. Different types of stakeholders hold both consensus and divergence regarding their views on FESs. For instance, there was strong consensus on services related to “natural ecotourism and biodiversity conservation”, while stakeholders expressed strong opposition regarding services related to “forest protection”. Furthermore, stakeholders elucidated the reasons behind their preferences for different types of FESs. Overall, our study indicates that besides considering the services provided by forests themselves, policymakers also need to pay attention to the preferences and divergences in needs among stakeholders of national parks. This ensures a more comprehensive fulfillment of diverse societal needs and facilitates the formulation of more effective policies to promote the sustainable management and conservation of national parks.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".