The Politics of ‘Green’ Extraction Frontiers: Mapping Metals and Mineral Mining Conflicts Related to the Energy Transition in the Americas
Bibliographic record
Abstract
We document how the extraction of metals and minerals, deemed critical for green growth and its energy transition, is expanding and being resisted in the Americas. Researchers and socio-environmental organizations co-produced 25 conflicts related to lithium, copper, and graphite mining. We examine mechanisms and discourses shaping the politics of ‘green’ extraction frontiers expansion. Governments and companies are promoting extraction in the name of an urgent planetary salvation. Socio-environmental movements claim that their territories are being turned into sacrifice zones, with an exacerbation of social vulnerabilities and impacts on sensitive and poorly known ecosystems, water, and cultural heritage sites. While criminalization and violence against local protestors is recurrent in the South, allegations of inadequate and poor decision-making and participation procedures occur across the continent. In Canada and the United States, fast-tracked permitting processes foster unrest. Global competition to secure access to critical materials is reconfiguring extraction frontiers, fueling resistance and creating tension on both globalization and deglobalization dynamics.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".