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Record W2750534418 · doi:10.4018/ijssmet.2017100103

Ivory Coast

2017· article· en· W2750534418 on OpenAlexaff
Kouame Joseph Arthur Kouame, Kouakou Alphonse Yao, Fuxing Jiang, Yu Feng, Sitao Zhu

Bibliographic record

VenueInternational Journal of Service Science Management Engineering and Technology · 2017
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLivelihoodBusinessOrder (exchange)Gold miningNatural resource economicsAgricultureGeographyEconomicsFinance

Abstract

fetched live from OpenAlex

In Ivory Coast, mining is one of the major sources of income for local people. Because of mining, jobs have been created thus increasing employment opportunities in rural regions. Moreover, this is a job that does not require a lot of skills, so a lot of people are able to join at the same time earning huge money within a short amount of time. Not only does this occupation attract adults, children are also interested in this activity. However, the negative social impacts caused by this activity remain indisputable. Chemical products used by miners and unsanitary conditions are harmful not only to miners themselves but also to innocent local people. There is a large destruction of lands, and also prostitution, which leads to the spreading of many contagious diseases. The paper mainly focuses on the impact of artisanal gold mining and its affects to local livelihoods and the environment in Ivory Coast. Some key recommendations for addressing artisanal mining activities in order to have good options for sustainable management of mineral resources in the country will be discussed.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.527
Threshold uncertainty score0.332

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.005
GPT teacher head0.215
Teacher spread0.209 · 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 designSimulation or modeling
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
Published2017
Admission routes1
Has abstractyes

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