Uncovering well-being ecosystem services bundles (WEBs) under conditions of social-ecological change in Brazil
Bibliographic record
Abstract
This research examines the interplay of a 3-dimensional well-being approach of coastal communities and the ecosystem services upon which they depend, and the implications for marine protected area (MPA) governance. We use the concept of well-being ecosystem services bundles (WEBs) to refer to the links among ecosystem services and social well-being as experienced by fishing communities adjacent to MPAs. This research combines data from surveys with households (n=59) and three participatory workshops (total participation n=48). We supplement results using insights from a photovoice process with community members (n=15) and participant observation (September 2018-April 2019). We identify key WEBs, social-ecological changes, and their trade-offs and synergies in three coastal communities on the southeast coast of Brazil. In doing so, we examine core WEBs relevant to coastal communities, and the drivers of change that influence these WEBs (e.g., increased tourism, deforestation) and show their dynamism and complexity. Further, we develop a typology to reflect how individuals perceive or experience the interplay among components of WEBs, or the “pathways of interaction” that connect their well-being to ecosystem services. Results reveal three key opportunities for improving MPA governance. First, we show that WEBs play a key role in perceptions of physical and public safety experienced in coastal communities, an insight that is especially relevant to the global south and developing countries due to the inequity-related security issues. Second, trade-offs in tourism are a major area for governance interventions to improve fit to the local context, such as enhancing the well-being of locals as it is shaping local livelihoods, culture, and social relations. Third, we develop a typology that highlights overlooked experiential, observational, and visual contributions of WEBs to well-being that have the potential to reinforce conservation values and stewardship actions in communities affected by MPAs.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".