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Record W2558320549 · doi:10.1111/lamp.12108

Canadian Mining Interests in Bolivia, 1985–2015: Trajectories of Failures, Successes, and Violence

2016· article· en· W2558320549 on OpenAlexaboutno aff
Vladimir Díaz-Cuellar, Kirsten Francescone

Bibliographic record

VenueLatin American Policy · 2016
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Latin AmericansPolitical scienceMining industryPoliticsWork (physics)Social capitalEngineering

Abstract

fetched live from OpenAlex

Over the past decade there has been a growing interest in and concern about the actions of Canadian mining companies in Latin America. In this article we contribute to these debates by combining economic, social, and political analyses to examine the development of the Canadian government and the role of Canadian‐headquartered companies in Bolivia's mining industry. First, we review the influence of the Canadian government's development assistance on Bolivian mining policy. Second, we analyze the characteristics of Canadian FDI and its effects on the Bolivian mining sector. We argue that the economic effects of Canadian mining companies in Bolivia have been less than significant. We consider it a failed attempt, since our data suggests that the Canadian government attempted to “make Bolivia work” for mining companies. Finally, we illustrate the specific trajectories of Canadian mining companies with four brief case studies, two mines in operation, and two “failed attempts.” In the first two case studies we examine the development and accumulation of capital. In the second two cases, we focus on the social conflicts, which arose around the exploration activities of two junior mining companies. We argue that junior companies are important to consider when surveying the Canadian government's role in the country.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score0.885

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.011
Science and technology studies0.0080.003
Scholarly communication0.0040.001
Open science0.0020.004
Research integrity0.0010.002
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 head0.005
GPT teacher head0.229
Teacher spread0.224 · 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 designObservational
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

Citations4
Published2016
Admission routes1
Has abstractyes

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