Bibliographic record
Abstract
The Canadian government is ordering some Chinese firms to sell their stakes in three small Canadian lithium miners, arguing that the investments pose a threat to national security. Sinomine (Hong Kong) Rare Metals Resources must sell its shares in Power Metals; Chengze Lithium International must divest from Lithium Chile; and Zangge Mining Investment must sell its ownership in Ultra Lithium. Other Chinese investments have passed Canadian government scrutiny in recent years. In February of this year, Zijin Mining Group acquired the Canadian firm Neo Lithium. And in 2018, Canadian fertilizer giant Nutrien sold its 24% stake in Chile’s SQM to China’s Tianqi Lithium. Canada is developing a critical minerals strategy to help it play a larger role in the battery supply chain. But Howard Klein, president of the lithium advisory firm RK Equity, calls the government’s recent decision counterproductive. He’d rather see funding for Canadian mining projects. “There are sticks
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".