Examining Canada's role in critical minerals global competition: a case study on nickel and rare earth elements
Bibliographic record
Abstract
With the significant increase in global demand for critical metals, including nickel and rare earths, competition in the market for these resources has become increasingly intense. Canada, a country rich in natural resources, plays a crucial role in this global competition and has a well-defined critical minerals strategy. This article proposes an analysis of Canada's strategy and its role in global competition, focusing on two critical minerals: nickel and rare earths. The research methodology is based on two case studies that follow several key aspects: the benefits of the critical resource, Canada's production and resources, and at the global level, trade exchanges, existing projects, market risks, and production chains. The study shows that Canada plays an important role in the global competition for critical metals - nickel and rare earths - primarily through the prism of its resources and recently launched national strategy. The critical minerals strategy aims to strengthen this position and carefully manage market risks, despite significant challenges such as price volatility, China's position and related dependencies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".