Critical raw materials partner Canada: an (almost) perfect match
Bibliographic record
Abstract
The European Union (EU) is aiming to strengthen its cooperation with like-minded countries to secure its supply of so-called critical raw materials. European Commission President Ursula von der Leyen considers Canada a "perfect match" - a resource-rich and reliable partner that shares the EU's geopolitical interests and sustainability goals. Canada is seeking to diversify its supply chains and counteract the influence of Chinese actors in its mining industry through a policy of friendshoring. To this end, the Canadian government has shown itself to be far more open to cooperation with the EU regarding raw material supply chains and key industries compared to the United States (US) government. It would be beneficial for both sides to deepen this cooperation. However, to truly make this partnership a perfect match, the EU should offer stronger financial incentives for the integration of European and Canadian industries, promote scientific exchange and technical collaboration, and advocate for robust corporate due diligence in supply chains. (author's abstract)
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".