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A Study on Ivory Coast with the Impacts of Artisanal Gold Mining on Local Livelihoods and the Mining Industry

2017· article· en· W2582889525 on OpenAlex
Kouame Joseph Arthur Kouame, Fu Xing Jiang, Yu Feng, Si Tao Zhu

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

VenueAdvanced engineering forum · 2017
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLivelihoodGold miningPovertyAgricultureMining industryBusinessNatural resource economicsEnvironmental degradationLocal communityOrder (exchange)SustainabilityEnvironmental planningEconomic growthGeographyPolitical scienceMining engineeringEngineeringEconomics

Abstract

fetched live from OpenAlex

In rural regions, mining is an activity that employs many people due to the fact that the barriers to entry are sometime trivial, with very low technology, capital fund and no specialized skills required. Many people including children into artisanal mining in Ivory Coast because they can earn higher incomes in mining than through other traditional activities such as agriculture, which is the main activity in the country. Artisanal mining contribute to reduce the abject poverty and it offers many others opportunities. However, this activity has many negative social impacts. Local people including miners are risking their life everyday due to the unsanitary conditions, prostitution, chemical contaminants, and alcoholism, and also the large degradation of lands. The main objective of this Paper is to understand how artisanal gold mining in the Ivory Coast affects local livelihoods and the environment. Some key recommendations for addressing artisanal mining activities in order to have a good option for sustainable management of mineral resources in the country are proposed.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.463
Threshold uncertainty score0.529

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 head0.008
GPT teacher head0.218
Teacher spread0.210 · 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