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Record W7109663215 · doi:10.5281/zenodo.17841873

The Anthropocene Americas: Contested Landscapes of Resource Extraction and Indigenous Resistance

2025· article· W7109663215 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typearticle
Language
FieldEngineering
TopicMining and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsAnthropoceneIndigenousScholarshipResistance (ecology)PoliticsPolitical ecologyResource (disambiguation)NarrativeState (computer science)

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.028
Scholarly communication0.0060.004
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.014
GPT teacher head0.238
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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