Why we need more criticality experts from mineral-producing countries: analysis of the geopolitical provincialization of critical minerals assessments
Bibliographic record
Abstract
Abstract This study examines the evolving study of mineral criticality and highlights a key gap: the limited involvement of experts from mineral-producing countries (MPCs), especially in the Global South. Bibliometric analysis of 101 critical minerals assessments and methodologies shows that the current criticality field of study reflects a narrow set of industrial priorities and risk perceptions, often framing MPCs’ development goals as supply chain risks. This phenomenon is referred to as ‘criticality provincialization’ in this paper. Findings suggest that mineral-consuming countries (MCCs) such as China and some Global North nations are moving away from country-agnostic criticality assessments and successfully localising the subject to their own realities. Together, they demonstrate that criticality is an important tool for asserting or defending a country’s manufacturing and industrial interests. The study reveals that the expert imbalance sustains foresight-driven policy in MCCs and reactive policy in MPCs, leading to long-term resource dependencies. Findings show that criticality designations typically exist parallel to a short-lived 3–5-year window of windfall resource rents for MPCs, which incentivises expanding mining operations but often fail to enable the development of downstream sectors. The study concludes that MPCs should adopt criticality as a field of study within mineral economics, build local expertise, and localise the concept to their own strategic, economic, and development priorities.
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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 | 0.025 | 0.114 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".