What is behind land use change in tropical forests? From local relations to global mining concessions
Bibliographic record
Abstract
The global population depends on mineral and agricultural products sourced from tropical forests, driving land use changes with widespread impacts to biodiversity, food-security, and forest integrity. Land use change is thus the result of decisions taken by farmers, collectives, and institutions with different cultural backgrounds who are influenced by social and market relations at local and global scales. As a result, multiple (often contested) worldviews shape the fate of tropical forests. Although the ultimate decision-makers on land use change are typically farmers from both Indigenous and non-Indigenous (Mestizos) background, we know very little about how the nature of multi-level social relations shapes the land use preferences of farmers from different cultural identities. Drawing from qualitative and quantitative data collected through ethnography and 321 interviews in the Amazon and Andean Choco in Ecuador, we found that large- and small-scale farmers’ land use decisions are shaped by social relations with ancestors and neighbors, markets and powerful mining companies. The importance attributed to those individuals and collectives who shape farmers’ land use preferences is often linked with group identity. Our data, collected and analyzed using mixed methods, show that parents and ancestors are important in the transmission of knowledge, prestigious neighbors are sources of inspiration, and Mestizos serve as role models for some Indigenous farmers. Mestizos report more self-reliance in their decision-making, whereas Indigenous identify government support as having a stronger influence on their land use decisions. Overall, guided by their most important reference groups, Indigenous practice smaller-scale and more diverse agriculture than Mestizos. Women, regardless of ethnicity, commonly keep the practice of crop rotation and count more on their ancestors for land use advice, while deforesting less than men. Finally, global market dynamics and mining companies active in the Amazon and Andean Choco, often supported by the government, exert strong influence over farmers’ land use decisions, moving them away from preferences grounded in local relations towards more extensive and less diverse land use practices. However, some Mestizo and Indigenous farmers’ collectives are prominent actors in the mining resistance, actively fighting to protect tropical forests. This research proposes a novel theoretical and methodological approach to understanding land use change from a relational perspective, in culturally diverse territories often targeted by multiple institutional interventions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".