Impacts of agricultural expansion on the resource availability of forest-dependent Indigenous communities in the Dry Chaco
Bibliographic record
Abstract
Agricultural expansion into tropical and subtropical forests threatens forest-dependent communities by disrupting their access to vital resources. We explored these impacts for over 400 Indigenous communities in the Argentine Dry Chaco, a deforestation hotspot due to agricultural expansion. Using participatory mapping, we estimated resource collection footprints for plants and animals, and integrated these with deforestation data mapped from satellite images to show that by 2021, communities had lost on average 21% of their forests. An ecosystem services supply index revealed that 33% of communities saw 10-35% reductions in resource availability in 2001-2021. We also found substantial increase in access restrictions (42%), and communities had to travel over 10 km further to reach natural water sources. These findings highlight the severe consequences of agricultural expansion on Indigenous communities in the Chaco and likely many other dry forest regions, emphasizing the need for policies to prevent ecological marginalization of forest-dependent communities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".