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Towards global cooperation in securing critical minerals: Game theory analyses of policy discourses from the United States, the European Union, South Africa and Australia

2025· article· en· W4416354139 on OpenAlexaff
Desire Runganga, Bishal Bharadwaj, Helen Cabalu, Peta Ashworth

Bibliographic record

VenueResources Policy · 2025
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsUniversity of Calgary
FundersMaine Community College System
KeywordsIdeologyTechnocracyGame theoryCorporate governanceSubsidyCritical theoryStackelberg competitionStewardship (theology)Evolutionary game theory

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.492
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.326
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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