Economics and Multi-Criteria Decision Support for Sustainable Cobalt Production
Bibliographic record
Abstract
Mining critical minerals in the Democratic Republic of Congo comes with tough governance hurdles, environmental damage, and deep socioeconomic strains. This study’s approach blends SWOT with PESTLE analysis, drawing on the insight of experts from 32 organizations in 11 countries, including Canada, France, Germany, India, Norway, Pakistan, Poland, Portugal, Romania, Spain, and the United States. This two‑pronged approach makes it easier to fully examine the structural, political, and socio‑environmental sides of the cobalt industry in the Democratic Republic of the Congo. The PESTLE analysis paints the DRC’s political climate as unstable and steeped in corruption, with social governance in deep crisis and environmental oversight falling far short. In the SWOT analysis, strengths at 14.49 and opportunities at 12.54 edge ahead of weaknesses at 12.95 and threats at 11.27 on average. This forms the foundation for the study’s conclusion: greater transparency in institutions, fairer labour market governance, and stronger environmental laws could help curb the extractive injustices tied to cobalt mining in the DRC.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".