Forest Food Harvesting in the Talamanca Bribri Indigenous Territory, Costa Rica: Ethnoecology, Gender, and Resource Access
Bibliographic record
Abstract
Although forest foods are important for health and cultural continuity for millions of Indigenous people, information regarding how people use and access these foods is lacking. Using a qualitative methodology informed by Bribri teachings, this thesis examined the ethnoecology of food harvesting in the Talamanca Bribri Indigenous Territory, Costa Rica. This project illustrates how access to forest food requires: access to multiple land patches, unique landscaping practices, and fostering relationships with non- human beings. By examining wild food consumption by household and generation in one community (Bajo Coen), this research shows how: wild food harvesting is widespread, the majority of youth consume wild food, sharing is fundamental to access wild food, and people consume wild food for many reasons including identity and dietary variety. By examining gender across multiple harvesting stages, this study demonstrates that no single harvesting stage was exclusive to members of one gender and that mixed gender harvesting groups were common; these findings challenge generalizations that women and men engage in different harvesting tasks and highlight the importance of gendered collaboration. This thesis makes applied contributions to ethnobiology and forest management. By analyzing how protected area (PA) regulations shape access to forest food, this thesis highlights how PAs can have negative impacts on: health, nutrition, teaching youth, quality of life, cultural identity, and on the land; these findings are important because they show why Biosphere Reserves need to do more work to ensure their managers support people’s rights to access traditional food. To better understand the macro-level factors that shape food access beyond PAs, this thesis evaluates the political ecology of land access. Findings illustrate how Bribri people’s history of engagement in an inequitable market economy, in concert with discriminatory state policies of land reorganization and management, has created significant hurdles for some people to access forest resources and to grow their own food. This thesis has generated its findings using methods based on Bribri teachings; as such, it: 1) increases awareness of Indigenous methodologies in ethnobiology and 2) generates information about harvesting that accurately represents Bribri people and how they understand the world
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".