Decarbonisation and Critical Materials in the Context of Fraught Geopolitics: Europe’s Distinctive Approach to a Net Zero Future
Bibliographic record
Abstract
The race to rapidly decarbonise and digitalise the global economy by 2030 to avoid temperatures rising above 1.5C has been subsumed by geopolitics that remains anchored in realist power struggles, now revolving around Sino-American hyper-competition. The Russian invasion of Ukraine further undermined interdependence and prompted unprecedented levels of economic statecraft. Access to indispensable minerals for a net zero future has thus become more securitised. The European Union (EU) has pushed back against bipolar geopolitics by utilising its normative, economic and regulatory power and strong networks of global institutional relations to maintain a competitive but working relationship with the People’s Republic of China (PRC). Such an approach may help broker broader global institutional collaboration to ensure that decarbonisation is for all, not just for the few.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.040 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".