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Record W2739183997 · doi:10.25222/larr.63

Policing Economic Growth: Mining, Protest, and State Discourse in Peru and Argentina

2017· article· en· W2739183997 on OpenAlexaff
Ariel Taylor, Michelle D. Bonner

Bibliographic record

VenueLatin American Research Review · 2017
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsBoomOpposition (politics)State (computer science)DemocracyLatin AmericansPolitical sciencePolitical economyContext (archaeology)EconomySociologyLawHistoryEconomicsPoliticsEngineering

Abstract

fetched live from OpenAlex

Since the 1980s, liberalized and newly stable markets have helped usher in an unprecedented mining boom across the Latin American region. However, despite the fact that this boom contributes to notable economic growth, protests in opposition to the expansion and practices of mining companies have also grown, often with violent results. How protests are policed matters, but more important for democracy is how state actors respond when violence is employed. We examine two instances of police repression of mining protests: one in Cajamarca, Peru, and the other in Catamarca, Argentina. We argue that, despite significant differences in context, there are important similarities in state discourse between countries. In particular, a vocabulary of protester wrongdoing and calls for a remedy of “dialogue” are employed in both cases as a way to facilitate the continuation and expansion of both mining and the repression of protests.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.006
Science and technology studies0.0030.005
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.064
GPT teacher head0.370
Teacher spread0.306 · 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 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

Citations20
Published2017
Admission routes1
Has abstractyes

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