Canada's Strategy for Key Critical Raw Materials. Case Study: Copper and Graphite
Bibliographic record
Abstract
Critical minerals are of strategic importance in the new economy based on innovation and sustainability, as their unique properties make them essential for a wide range of advanced technologies. The expansion of technology and the demand for high-tech products are driving major world powers to actively seek access to a stable supply of rare minerals, especially in light of the overdependence on some countries or regions for key-resources. Canada is one of the major global players in the production and supply of critical minerals and has recognized their importance through country strategies. This article proposes a qualitative approach, an analysis of two of the most important key minerals –copper and graphite – that are priorities for Canada's critical resource strategy. The research is based on documentary analysis, the results of which are presented in the form of two independent case studies focusing on aspects such as mineral description and use, supply chain, production, Canada's access to resources, and trade. The global competitive component with China, the main player in most critical rare metals markets, is also considered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".