Artificial Intelligence and Machine Learning in Governmental Artisanal Mining: Current Status, Development, and Future Directions
Bibliographic record
Abstract
The COVID-19 pandemic is not an obstacle research and development implementation, one of which uses secondary data and bibliometric methods. Studies on mining regulation are generally about formal mining in the form of corporations, while artisanal mining is considered illegal, criminal, and its operation is prohibited because it inhibits the growth rate of a country’s socio-economic development. This study aims to analyse previous studies on governmental artisanal mining published in the Scopus database and data processing using VOSviewer software. The findings show that there are 287 documents on governmental artisanal mining published from 1987 to 2023. United Kingdom, Canada, and United States occupy most countries of publication as the place of author affiliation. Meanwhile, the author who produced the largest number of publications and is cited mostly was Galvin Hilson. The top ten publications based on the number of citations were obtained by the majority of journals ranked in Quartile 1 with the top rankings being Resource Policy Journal, Journal Cleaner Production, and Science of the Total Environment Journal. The dominant keywords used by authors were “artisanal and small-scale mining”, “formalization”, “illegal mining”, “Ghana”, and “gold”. The data revealed that there are still limited studies discussing the link between the governmentality of artisanal mining and local politics, other mining, and identity, as well as its relationship with the COVID-19 pandemic. Future studies can further develop the case of governmental artisanal mining from a social critical perspective and in comparison with other types of mining across countries.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".