Shadows of Extraction, Beacons of Resistance: 33 Communities’ Struggle for Justice in Salinas Grandes
Bibliographic record
Abstract
In Argentina’s Salinas Grandes salt flat, more than 33 Indigenous and pastoral communities have mobilized to resist and halt lithium extraction on their land. As a critical mineral used in electric vehicle batteries, the demand for lithium has skyrocketed in recent years. Drawing on James C. Scott’s framework of slow resistance in his book Weapons of The Weak: Everyday Forms of Peasant Resistance, this study explores how acts of resistance such as slander, sabotage, and foot-dragging have evolved through networked advocacy and digital communication. These acts of resistance—roadblocks, legal challenges, and counter-knowledge production—accumulate to disrupt extractive operations and challenge neoliberal governmental and corporate power structures. Unlike Scott’s original case study, these tactics are no longer isolated or anonymous. Collaborations with NGOs, media campaigns, and international networks amplify them. The communities’ opposition highlights lithium extraction's less visible socio-ecological costs, reframing the sustainability narrative to prioritize human rights, water security, and Indigenous self-determination. Ultimately, the project underscores how local resistance and dissent challenge extractive practices and reshape global conversations about justice, sustainability, and the true costs of the green transition.
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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 | 0.043 | 0.025 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".