MétaCan
Menu
Back to cohort
Record W4411051202 · doi:10.53328/inr25sal004

Building a Global Minerals Trust for a Just Green Transition

2025· report· en· W4411051202 on OpenAlexaffabout
Saleem H. Ali, Miriam R. Aczel, Kaveh Madani

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEnvironmental Science
TopicSustainable Development and Environmental Policy
Canadian institutionsUnited Nations University Institute for Water, Environment, and Health
Fundersnot available
KeywordsTransition (genetics)AstrobiologyEarth scienceChemistryGeochemistryBusinessGeologyPhysics

Abstract

fetched live from OpenAlex

"Today, more than 70% of global production for key critical minerals is concentrated in just a few countries, raising serious concerns about supply security, market volatility, and geopolitical risk. Achieving a just and sustainable energy transition hinges on fair and reliable access to critical minerals—materials key for low-carbon technologies. However, global supply chains remain environmentally damaging and vulnerable to geopolitical tensions, creating systemic risks for both climate and economic goals. • A Global Minerals Trust offers a new multilateral model to promote responsible stewardship, fair pricing, and secure equitable access to strategic minerals--balancing national sovereignty with planetary responsibility. • The Trust can advance a just and circular transition by enabling pooled investment, transparent trade, mineral recycling, and benefit-sharing with resource-producing nations, particularly in the Global South. • Global cooperation through platforms such as the G7, G20, IGF, and United Nations is essential to coordinate action and build a resilient, inclusive, and future-proof minerals governance system. • Canada’s 2025 G7 presidency offers a strategic opportunity to facilitate early-stage consensus around the Trust, drawing on its strengths in environmental diplomacy and multilateral engagement."

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.102
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.025
GPT teacher head0.296
Teacher spread0.272 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same topicSustainable Development and Environmental PolicyFrench-language works237,207