Global Research Dynamics: A Bibliometric Exploration of Child Education in Artisanal Mining
Bibliographic record
Abstract
This study investigates the trends in child education and artisanal mining research over the past two decades, aiming to provide a quantitative analysis of the network structure within these studies. The research utilizes a bibliometric method to analyze scientific publications from the Scopus database, with results visualized using VOSviewer. A total of 208 documents from multiple countries were examined. The findings reveal that Galvin Hilson is this field's most prolific and influential author. The United Kingdom leads in terms of publication volume and citations, followed by Canada and China. The Journal of Extractive Industries and Society is the most prominent journal by volume, while the Journal of Science of The Total Environment stands out for having the highest number of citations (930). The most cited article is “Contamination Features and Health Risk of Soil Heavy Metals in China” by Chen et al. The dominant topic explored is artisanal and small-scale gold mining in Sub-Saharan Africa. While child labor remains a primary focus, the relationship between education, poverty, and socioeconomic improvements is underexplored. This study highlights the need for further research on how socioeconomic changes influence children’s education and the broader context of artisanal mining across developing nations.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.020 | 0.104 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".