The Anthropocene Americas: Contested Landscapes of Resource Extraction and Indigenous Resistance
Bibliographic record
Abstract
The concept of the Anthropocene, positing humanity as a dominant geological force, finds stark and critical expression across the Americas. This paper critically examines the Anthropocene epoch through the lens of ongoing resource extraction activities and the persistent, often violent, resistance by Indigenous peoples. Focusing on diverse American landscapes – from the Amazon rainforest to the Canadian tar sands and Andean mining regions – we analyze how settler-colonial legacies and global capitalism converge to accelerate environmental degradation and dispossess Indigenous communities of their ancestral territories. We argue that the Anthropocene in the Americas is not merely a geological phenomenon but a deeply political and social one, shaped by contested notions of land, sovereignty, and development. Through an interdisciplinary approach drawing on political ecology, critical geography, and Indigenous studies, this research synthesizes current scholarship and provides a framework for understanding the complex interplay between extractive industries, state policies, and Indigenous movements. The paper highlights the profound socio-ecological transformations wrought by extraction and underscores the critical role of Indigenous resistance in articulating alternative pathways for human-earth relations, challenging the dominant narratives and practices of the Anthropocene.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| 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".