Indicators of illicit activities in gold supply chains: Insights from Peru and Kenya
Bibliographic record
Abstract
Illicit gold flows constitute a major development challenge for governments and a social responsibility challenge for the jewellery industry, which accounts for the majority of global gold demand. This paper highlights aspects of gold supply chains that lack transparency and may indicate junctures where illicit activities are taking place, resulting in a loss of tax and customs revenues. Using Peru and Kenya as case study countries, we draw from United Nations Comtrade data and qualitative data from field research to examine the magnitude of the gold trade, the forms in which gold is traded, and key trade partners for each country. We suggest that midstream portions of gold supply chains should be given more attention, certain types of gold exports and imports present greater traceability challenges than others, and some countries play a much more significant role in the global jewellery trade. We propose areas where further investigations may be warranted to ensure more transparent and responsible gold supply chains.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".