Diverse values regarding nature are related to stable forests: the case of Indigenous lands in Panama
Bibliographic record
Abstract
Local land use emerges from peoples’ worldviews and values regarding nature. In neotropical forest landscapes, largely inhabited by Indigenous peoples, exploring how Indigenous land use and underlying values may converge with global values such as carbon sequestration and biodiversity conservation may provide lessons to achieve equitable ecological and social outcomes. However, most studies have focused on exploring the influence of Indigenous land use on avoiding deforestation, while few examine how local values relate to deforestation, disturbances, and forest cover stability. To address these gaps, we analyzed deforestation and disturbance spatial-temporal patterns in Indigenous lands in Panama between 2000 and 2020, using a continuous change detection algorithm and generalized additive models. Additionally, we performed participatory mapping across three Indigenous lands to identify instrumental and relational values linked to land use. Our results show that disturbances followed by recovery are the dominant cause of land cover changes in Indigenous lands. Moreover, the area of stable forest cover in Indigenous lands until 2020 was two times higher than in protected areas and other lands lacking protection. The generalized additive models demonstrate that deforestation and disturbance in Indigenous lands exhibit a low density, spatial concentration on forest edges, and temporal stability, explaining forest cover stability. According to participatory mapping, obtaining food from agriculture mainly occurs where deforestation and disturbance are more concentrated. In contrast, other instrumental (i.e., gathering food and household materials) and relational values (e.g., sacred sites) are more dispersed in forests. By weaving scales and perspectives, our results illustrate that diverse values regarding nature framed by Indigenous worldviews can beget stability to forest cover, contributing to Indigenous peoples' quality of life, climate change mitigation, and biodiversity conservation. To align these contributions with global climate and biodiversity targets, it is crucial to disarticulate land ownership from deforestation, grant formal titles to Indigenous lands, and foster equitable incentives to Indigenous peoples.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".