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Record W2884738867 · doi:10.15273/ijge.2018.03.017

Social Dimension of the Successful Development of Mining Projects – a Focus on Artisanal and Small-Scale Mining

2018· article· en· W2884738867 on OpenAlexvenueno aff
Nicole Smith, Juan Lucena, Jessica M. Smith, Óscar Jaime Restrepo Baena, Gustavo Aristizábal, A A Wilman Delgado

Bibliographic record

VenueInternational Journal of Georesources and Environment · 2018
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsLivelihoodSAFERScale (ratio)Latin AmericansDimension (graph theory)Sustainable developmentSociotechnical systemEconomic growthEnvironmental planningPolitical scienceBusinessGeographyAgricultureEconomicsComputer scienceManagement

Abstract

fetched live from OpenAlex

In Colombia, Peru, and other Latin American countries, different scales of mining activity usually develop in areas with high social, economic, and environmental complexity. Artisanal and small-scale gold mining (ASGM) is one mining sector that continues to grow and pose challenges for governments, industry, communities, and academics. Although numerous attempts have been made to intervene in this sector and implement cleaner, safer, and more environmentally friendly technologies, the majority of these initiatives have been relatively unsuccessful for they have been founded on myopic understandings of ASGM and the perspective that technology is a silver bullet for addressing the problems associated with ASGM. The complexity of ASGM warrants a different research approach. This paper provides an example of a framework that is being applied to research and engineering education on ASGM. The framework is highly interdisciplinary, international, inter-institutional, and intergenerational in nature. We contend that this type of approach is necessary to support ASGM in becoming a more sustainable livelihood for rural communities in the developing world.

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)0.000
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 score0.614
Threshold uncertainty score0.255

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.011
GPT teacher head0.202
Teacher spread0.191 · 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 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

Citations5
Published2018
Admission routes1
Has abstractyes

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