Illegal Gold Mining in the Brazilian Amazon: Environmental Degradation in Yanomami Indigenous Lands, and Regulatory Failures
Bibliographic record
Abstract
Illegal gold mining in the Amazon poses a severe threat to indigenous communities and the environment. This study quantitatively investigates the link between gold exports, mining expansion, and resultant deforestation within the Yanomami Indigenous Territory from 2008 to 2022. Utilizing a quantitative, empirical approach with Ordinary Least Squares (OLS) regression models. The findings reveal a direct and statistically significant correlation between economic drivers and environmental degradation. A 1% increase in the value of gold exports corresponds to an approximate 1.6% expansion of the mined area in Yanomami lands. This mining activity is a direct catalyst for deforestation, with a 1% increase in mining leading to a nearly 0.57% rise in deforested areas. Conversely, the study identified a protective effect of local economic conditions, where a 1% increase in local GDP per capita is associated with a 3.03% decrease in mining pressure. The research concludes that the international demand for gold, coupled with inadequate regulatory oversight in Brazil, directly fuels the humanitarian and environmental crisis in the Yanomami territory. Major importing nations, such as Canada, Switzerland, and the United Kingdom, share a significant responsibility due to their role in the global gold supply chain and the acceptance of gold with illegal origins. The study underscores the urgent need for enhanced due diligence and robust traceability mechanisms to mitigate the devastating impacts of illegal mining on indigenous populations and vital ecosystems. Future research should explore the role of financial institutions in this illicit trade and incorporate qualitative perspectives from the affected 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".