Exploring Plural Values of Ecosystem Services: Local Peoples’ Perceptions and Implications for Protected Area Management in the Atlantic Forest of Brazil
Bibliographic record
Abstract
The remnants of the Atlantic Forest in Brazil are significant for biodiversity and provide benefits for people (climate regulation, water supply, health and welfare, among others). However, nature’s importance for different people may vary, for social, environmental, and economic reasons. In this paper, we explore such differences among people living in communities surrounding the Cunhambebe State Park (PEC), a large area of Atlantic Forest. We assess their perceptions regarding the plural values of ecosystem services derived from the PEC and explore ways in which this could affect the management of this protected area. Our assumption is that analyzing the perceptions of people who live in the communities surrounding can be a key tool for the formulation of proposals to improve management models and address socio-environmental conflicts. Based on interviews, participant observation, and document analysis, our results show a direct link between culture and environment since relational values and cultural ecosystem services are closely related to local people’s valuation of the PEC. Therefore, we support management strategies which are based on local values for land and forest use in a sustainable way. Our findings may contribute to decision making by PEC managers, governments, local stakeholders, and researchers.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".