Critical minerals and countries' mining competitiveness: An estimate through economic complexity techniques
Bibliographic record
Abstract
Minerals' criticality and countries' mining competitiveness are two dimensions that have gained relevance in the economic and policy agenda due to the key role of minerals in the energy transition. To a certain extent, these product-country dimensions can be seen as two faces of the same coin, which intertwine and simultaneously co-determine each other. Therefore, economic complexity techniques appear as a useful methodology to simultaneously estimate both dimensions. This paper employs economic complexity techniques to build an unsupervised Fitness-Criticality algorithm, that allows simultaneously estimating countries' mining competitiveness (Fitness Mining Index) and minerals' criticality (Criticality Minerals Index). Our indexes are efficient in terms of the set of information employed, and do not rely on subjective perspectives and assessments. The results of the estimates suggest that South Africa, Russia, the United States, Norway, Canada, Australia and Chile are the most competitive countries. Moreover, the Platinum Group Metals, Lithium, Silicon and Rare Earths appear as the most critical minerals. These results are consistent with other methodologies employed by different organizations that separately estimate both dimensions and derive countries' and minerals' rankings.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".