Clean and future-oriented: Local perceptions of lithium extraction in Bolivia during the presidency of Evo Morales
Bibliographic record
Abstract
In the past two decades, lithium has gained critical global importance as a transition metal. Under President Evo Morales (2006–2019), the Bolivian government launched a national lithium extraction industry in the Uyuni salt flat. However, efforts to develop industrial-scale extraction of lithium there have been beset by considerable delays. Focusing on the period of Morales’ presidency, this article analyses the perceptions of lithium and its extraction amongst people living in the region around the Uyuni salt flat, specifically in the urban centres. In state media and official communication lithium extraction was presented as a ‘clean’ and ‘future-oriented’ activity, distinct from traditional mining practices. Public perceptions of lithium extraction as being ‘future-oriented’ and distinct from conventional mining practices were also rooted in collective memories of the colonial and neoliberal past and the exploitation of Bolivia's wealth by foreigners. Lithium extraction was therefore also associated with a shift towards a decolonial future that was expected to generate wealth. By the end of the Morales presidency, while people in the region still believed in the clean and future-oriented nature of lithium and its extraction, they no longer believed in Morales’ ability to generate national or local benefits from lithium production.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".