Gold Supply Chain Opacity and Illicit Activities: Insights from Peru and Kenya
Bibliographic record
Abstract
Illicit gold flows constitute a major development challenge for governments and a social responsibility challenge for many industries along gold supply chains, including gold refiners and jewelry retailers. 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, discrepancies in reported trade data, and key trade partners for each country. We suggest that certain portions of gold supply chains should be given more attention, some types of gold exports and imports present greater traceability challenges than others, and some countries play a much more significant role in the global gold 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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".