Building a Global Minerals Trust for a Just Green Transition
Bibliographic record
Abstract
"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 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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".