A Participatory Assessment of Ecosystem Services and Human Wellbeing in Rural Costa Rica Using Photo-Voice
Bibliographic record
Abstract
Human well-being is intricately connected to ecosystem services. A keystone contribution to the ecosystem service literature has been the Millennium Ecosystem Assessment, MA, (Ecosystems and human well-being: a framework for assessment, Island Press, Washington, DC; 2003, 2005). Much of the work on ecosystem services to date has focused on the assessment and classification of environmental functions. The need for inclusion of community perspectives in ecosystem assessments has been widely recognized in order to better understand the distribution of impacts and benefits resulting from natural resource use. Communities can offer a direct route to understanding the complex relationships between ecosystems and human well-being and how environmental management affects their livelihoods. Photovoice has been made popular as a tool for participatory needs assessment but it has had limited use in ecosystem assessments to date. The purpose of this paper is twofold: (1) to present the results of a community-level assessment of environmental services in a watershed dominated by pineapple monoculture in Costa Rica; and (2) to evaluate the strengths and the limitations of photovoice as a tool for mapping the relationship between ecosystems and people. I argue that photovoice is an underutilized methodology that has the potential to complement biophysical ecosystem service assessments in the context of impoverished and resource-dependent communities, particularly, since assessing ecosystem services and acting upon that information requires integrating the knowledges of diverse stakeholders, recognizing power imbalances, and grappling with the complexity of social-ecological systems. Processes such as photovoice have the potential to catalyze community self-organization, which is a critical component for empowerment.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.000 |
| 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".