Evasion of Children in Ivory Coast Artisanal Mining Activities
Bibliographic record
Abstract
The development of the mining industry is necessary for the national GDP growth. The gold mining operation provides great support to local people in the construction of roads, hospitals and schools. However the damage caused due to the illegal gold mining in Ivory Coast has become increasingly worrying. Thousands of miners unlawfully exploit gold in many parts of the national territory. The local people, especially the children see artisanal gold mining as a faster way to get out of the growing poverty. According to the investigation with local people, MDA, mining companies, the rebellion in 2002 and the post-election crisis in 2010 were a key issue. As result of the political unrest many children have left school to move into the mining activities. This paper focuses on some existing problems relating to the minors in artisanal gold mine as well as how the illegal gold mining activities should increasingly concern the state’s authorities who have to display their determination to stop this recurring phenomenon. In this paper, some suggestions will be proposed and we also support some initiatives and actions of the current government in order to reduce the rate of children or if possible to withdraw all the children from mining sites. The World Bank, financial institutions, NGOs are appealing too to play a major support role to eradicate child labor and to protect children in Ivory Coast and over the world.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".