Blockchain Technology in Mineral Extraction Supply Chains: A Comparative Study of DRC and Botswana Contexts
Bibliographic record
Abstract
Blockchain technology is gaining traction in addressing transparency challenges within mineral extraction supply chains globally. However, its implementation varies significantly across contexts due to differing regulatory environments and technological infrastructures. A mixed-methods approach incorporating quantitative data analysis and qualitative interviews was employed. Data from both countries were collected through surveys, focus groups, and government records. Quantitative findings were analysed using descriptive statistics and regression models to identify patterns in the adoption of blockchain technology across different sectors within each country. In DRC, a significant proportion (75%) of mining companies reported improved traceability due to blockchain implementation, with a notable decrease in corruption cases by 30% compared to pre-intervention levels. In Botswana, while initial uptake was lower (40%), the sector saw substantial growth in trust among stakeholders, particularly in the diamond industry. Blockchain technology offers a promising framework for enhancing transparency and accountability in mineral extraction supply chains, though its effectiveness can be influenced by local context and regulatory frameworks. Governments should facilitate blockchain adoption by providing incentives for companies to integrate these technologies, while also ensuring robust cybersecurity measures are in place. Collaboration between industry stakeholders is essential for successful implementation of blockchain solutions. mineral extraction, supply chain transparency, blockchain technology, DRC, Botswana Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".