The Pandemic’s Golden Touch: (Neo)Extractivism, Coloniality, and Necropolitics on Brazil’s Indigenous Territories
Bibliographic record
Abstract
Abstract Mining has been at the forefront of coloniality for hundreds of years in Brazil, representing one of the main threats to the integrity and health of Indigenous lands. The 1988 Brazilian Constitution recognized Indigenous peoples’ rights to the lands they occupy, and their natural resources, according to their traditions, uses, beliefs, and practices. Constitutional provisions, however, have not impeded governments and lawmakers from actively enabling extractive activities in Indigenous territories and their surroundings. Recently, the Bolsonaro government proposed a package of laws and policies to legalize mineral exploitation on Indigenous lands, using the economic uncertainties generated by the COVID-19 pandemic as a justification. However, this action must be explained through the paradigms (or philosophical frameworks) of the extractive economy and coloniality of power, operationalized by necropolitics. The article’s main argument is that the Constitution requires the government to engage in practices of decoloniality that express Indigenous legal traditions. Even though a newly elected government has been revoking many of Bolsonaro’s proposals, the paradigms of the extractive economy and the coloniality of power have a profound, structural influence on the Brazilian legal and political systems and must be challenged by a revival of decolonial ways of thinking and acting.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".