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Record W6989863196

Chinese Mining and Indigenous Resistance in Ecuador

2023· article· en· W6989863196 on OpenAlexaboutno aff

Bibliographic record

VenueFlorida International University Digital Commons (Florida International University) · 2023
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousPoliticsLatin AmericansReputationSociocultural evolutionResistance (ecology)Investment (military)Indigenous rights
DOInot available

Abstract

fetched live from OpenAlex

Chinese mining companies were drawn to Ecuador by a strong interest in diversifying their sources of copper in Latin America. But Chinese mining operations in Ecuador, which could have contributed to Ecuadorian development, soon gained a negative reputation after these activities prompted a great deal of local pushback, especially from affected Indigenous communities. As a result, the major Chinese mining consortium that now controls Ecuador’s two main copper mines has taken adaptive steps to stabilize its mining investment and increase the security of its supply networks, steps that often have not produced the intended results. Working through two subsidiaries, the Chinese mining consortium has responded to this localized criticism with a blend of tactics that includes co-opting select local figures, colluding with national officials to sidestep environmental and sociocultural safeguards, and coercing inhabitants into relocating under the threat of force from accommodating Ecuadorian authorities. By turning Ecuadorian national elites against locals and using divide-and-conquer tactics among Indigenous communities, the Chinese-led mining projects have entrenched existing political cleavages, have undermined community cohesion, and ultimately have harmed Ecuador’s democratic fabric, especially the standing of civil society and Indigenous rights organizations. While Ecuador has welcomed Chinese capital and other sources of international investment, this infusion of financing has increased the risk of political abuses at the national and local levels. This paper explains the adaptive strategies employed by the Chinese consortium and its subsidiaries in charge of the Mirador and San Carlos Panantza mining sites, contrasting the differing results these tactics have produced in each case. Both projects are located in Ecuador’s so-called Copper Belt provinces of Morona Santiago and Zamora Chinchipe, which are part of a mountain range known as the Cordillera del Cóndor. They are embedded in an ecologically and culturally sensitive zone that includes territory of the Indigenous Shuar community in the Ecuadorian Amazon. In the case of Mirador, the Chinese mining consortium’s adaptive response helped its subsidiary overcome local resistance but only by crushing it. In the case of San Carlos Panantza, local resistance so far has not been overcome, so the Chinese consortium has remained unable to proceed with its project. Neither case, even the Mirador site where mining has moved forward, is a sign of success for future relationships between Chinese mining conglomerates and Ecuadorian communities. To understand why the Chinese consortium’s adaptive tactics were somewhat more successful in Mirador, it is important to focus on the differing composition of the inhabitants of the land where the two mines are located. Mirador sits on territory shared by Shuar and non-Shuar settler communities who have different bonds with the land. The non-Shuar settlers emphasize the productive and 2 commercial value of the land over the spiritual and symbolic value that is key for many in the Shuar community. The Canadian-held and later Chinese-controlled companies active in Ecuador’s mining industry understood this difference between Mirador’s inhabitants and adapted accordingly: they managed to displace resistant residents despite widespread opposition through questionable and sometimes arguably illegal purchases of land. In San Carlos Panantza, a second subsidiary of the Chinese consortium chose to respond to local criticism with the same alleged practices of violence, occupation, and displacement used in Mirador. However, although the two projects are geographically near each other, the situation played out differently at the second would-be mine: ongoing opposition has prevented mining operations from beginning at all yet. Again, paying attention to the inhabitants of the land is instructive. San Carlos Panantza is in the heart of Shuar territory in Arutam, a region with few non-Indigenous settlers. The mining incursion by the Chinese-run subsidiary and the Ecuadorian security forces tasked with supporting it were seen as a threat to the area’s Shuar people, who have been strongly protesting and opposing the mining venture since late 2016. This state of affairs is likely to have far-reaching effects for Ecuador too. The apparent collusion between Ecuador’s national government and the Chinese consortium (and its subsidiaries) has crushed those who oppose mining, has upended the country’s policies on resource extraction, and has yielded documented violations of local communities’ human rights. These events have transpired because both the Chinese firms and the Ecuadorian state have tended to see local communities as an obstacle to the development of the country’s extractive industries. As a result, local social and environmental safeguards have been weakened, tenuous consultation processes have eroded, environmental licenses have been granted under dubious circumstances, and local communities have been forcibly displaced. This paper explores the implications of the adaptive tactics chosen by the Chinese mining subsidiaries that run the Mirador and San Carlos Panantza mines. It also addresses how Chinese companies have, in some cases, negotiated with local communities to begin mining exploitation, while also analyzing the ways the Chinese mining consortium has interacted with the Ecuadorian government and other players, such as the Canadian mining company it acquired and other peer companies that set up successful coalitions for mining development in Ecuador. Finally, the paper explores the effects the agreements between the Ecuadorian government and the Chinese consortium have had on local actors.

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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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.741
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.007
GPT teacher head0.186
Teacher spread0.178 · 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.

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

Citations3
Published2023
Admission routes1
Has abstractyes

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