Towards global cooperation in securing critical minerals: Game theory analyses of policy discourses from the United States, the European Union, South Africa and Australia
Bibliographic record
Abstract
This paper analyses over 2000 critical minerals policy submissions from the United States, the European Union, Australia, and South Africa (2010–2024). Integrating game theory with critical discourse analysis reveals how four dominant discursive strategies—securitisation of dependence, recontextualisation of exclusion, moralisation of domestic hierarchy, and moralisation of unilateral rule-setting—map onto specific non-cooperative game archetypes: the Prisoner's Dilemma, Stackelberg Followership, Assurance Game and Stag Hunt. Together, these strategies form a multi-level game that incentivises subsidy races and regulatory arbitrage, pushing the global system toward suboptimal outcomes of underfunded and inequitable value chains. To escape this equilibrium, the paper proposes an Integrative Mineral Criticality (IMC) framework to reconceptualise global governance of critical minerals as a cooperative game. • Global discourses on critical minerals (CMs) reveal competing ideologies (e.g hierarchy, exclusion, redress) that shape cooperation. • The ideologies map into distinct game archetypes (e.g Prisoner’s Dilemma, Stag Hunt), leading to suboptimal outcomes. • We propose a WTO CMs Technocratic Secretariat that sets and verifies scientifically tiered ESG standards, technology transfers and SDT mechanisms to sovereign capability.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".