African countries in the rare earth metals market: outsiders or independent players?
Bibliographic record
Abstract
Relevance. Rising demand for rare earth elements (REE), coupled with China’s dominance in reserves and processing, is driving Western countries to seek alternatives in Africa. Although officially recorded African reserves account for less than 5% of the global total, including unrecorded deposits the continent may hold about one-third of the world’s supply. Research Objective. The study aims to determine the position of African countries in the global REE market amid intensified competition between ‘old’ players (EU, United Kingdom, Australia, Canada) and ‘new’ players (USA, China). Data and methods. In addition to monographs and research articles, the study uses primary and secondary statistical data and employs comparative cross-regional and cross-national analysis. The research follows the technological chain of REE production, from Africa to global markets, covering the period from 1952 to mid-2025. Results. Although metal production is still virtually absent in African countries and only the lower segments of the technological chain have developed, African countries, primarily South Africa, are asserting themselves as independent actors in global rare earth markets. This trend is facilitated by higher returns from foreign investment in rare earth mining in Africa, amid the Juglar-type cyclical fluctuations of market prices and the current upward phase of the price cycle. Conclusions. In the near future, Morocco, Nigeria, Burundi, and Zambia are expected to become the main arenas of competition between the ‘old’ and ‘new’ players for REEs in Africa. The first two are likely to fall under Western influence, the latter two under China’s.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".