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Record W4320495185 · doi:10.1111/beer.12522

Conflicts between mining companies and communities: Institutional environments and conflict resolution approaches

2023· article· en· W4320495185 on OpenAlex
Chang Hoon Oh, Jiyoung Shin, Shuna Shu Ham Ho

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBusiness Ethics the Environment & Responsibility · 2023
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsDalhousie University
Fundersnot available
KeywordsLicenseConflict resolutionOrder (exchange)BusinessSocial conflictDue diligencePublic relationsPolitical scienceFinanceLaw

Abstract

fetched live from OpenAlex

Abstract Although companies recognize the importance of social responsibility and community engagement, conflicts between companies and communities have been noticeably increasing. To better understand the role of institutional environments in company–community conflicts, we analyze two mining conflicts—Minera Yanacocha's Minas Conga extension project in Peru and Minera Los Pelambres' El Mauro Tailings Dam in Chile. Our findings imply that, to prevent negative consequences and alleviate company–community conflicts, mining companies should address underlying structural causes and pursue informal approaches in order to obtain and maintain their social license. We find that better formal institutional environments not only alleviate conflict intensity but also facilitate informal approaches through which companies and communities can cooperate to resolve conflicts. The best practice would be to start and continue dialogs between communities and companies, mediated by impartial governments, to understand the concerns of the counterparty and find means by which to address the causes.

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.

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)0.003
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.725
Threshold uncertainty score0.806

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.150
GPT teacher head0.260
Teacher spread0.110 · 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