Landscape Ethnoecology of Forest Food Harvesting in the Talamanca Bribri Indigenous Territory, Costa Rica
Bibliographic record
Abstract
There is a vast literature on Bribri people's food harvesting, but this literature has largely overlooked how Bribri people interpret their food harvesting practices. Using a landscape ethnoecology approach, we worked with Bribri colleagues to describe forest food harvesting in one community (Bajo Coen) within the Talamanca Bribri Indigenous Territory in Costa Rica. Sylvester spent nine months living and harvesting food with Bribri people, and carried out semi-structured interviews and focus group discussions to gather information. Our study revealed that harvesting food requires interacting with non-human beings to ensure harvesting is respectful of other Bribri worlds and Sibö's (the Creator) teachings. We also illustrate how harvesting and cultivating food in the forest is important to keep the land alive. Our study further revealed how farm and forest land patches are linked through Bribri harvesting. People plant cultivated species in forests and transplant wild species into farms. These practices are important to access food, to encourage animals in spaces near dwellings, and to keep the land alive. Lastly, we illustrate spatial and temporal links among the following activities: 1) polyculture and wild harvesting (of both plants and animals), 2) shifting agriculture and harvesting wild edible greens, and 3) hunting and harvesting wild greens. Our results are relevant to forest management because we provide information about Bribri harvesting practices that forest managers have committed to supporting but have reported lacking the information to do so.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".