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Record W4416747048 · doi:10.1016/j.exis.2025.101820

Development in the context of electrification and the lithium industry: The view of the citizens of San Pedro de Atacama

2025· article· en· W4416747048 on OpenAlexafffund
Eduardo Ordonez‐Ponce, Mauricio Lorca

Bibliographic record

VenueThe Extractive Industries and Society · 2025
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsAthabasca University
FundersAthabasca University
KeywordsIndigenousState (computer science)Context (archaeology)Climate justiceEconomic JusticeDominance (genetics)Face (sociological concept)Capital (architecture)Electrification

Abstract

fetched live from OpenAlex

• Most citizens do not oppose mining outright but see its impacts as largely negative. • Mining views are shaped by weak state presence and corporate influence. • Support for mining depends on economic and socio-environmental gains and impacts. • State absence and capital dominance have shifted justice priorities. • Views vary both within and between Indigenous and non-Indigenous groups. In the face of the climate emergency, lithium is seen as essential for the green transition. Yet, the voices of those living in lithium-rich regions have been excluded from discussions about the industry's role in their development. Drawing on socio-environmental justice literature, this study interviewed Indigenous and non-Indigenous citizens from San Pedro de Atacama in the "Lithium Triangle" region to explore their perspectives on lithium mining. While most do not oppose the industry outright, their support is conditional fundamentally on economic benefits. Despite generally negative views on mining’s impacts, many adopt a pragmatic stance, engaging with companies to secure tangible benefits amid deteriorating environmental conditions and social tensions leading to symbiotic relationships. Their responses reflect broader dynamics shaped by state neglect and corporate influence that shape the region’s path towards or away from socio-environmental justice.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score0.278

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.228
Teacher spread0.216 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations2
Published2025
Admission routes2
Has abstractyes

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