Bibliographic record
Abstract
In recent decades, China has moved up among the countries that are Canada's main trading counterparties and today is its second most important economic partner after the United States. At the same time, for China, Canada's role as a supplier and a market is not so great. To the Chinese market Canada supplies mainly raw materials, agricultural and forestry products. Moreover, over the past 20 years, the structure of Canada's exports to China has changed markedly in favor of the raw materials sector. The opposite trend is characteristic of Chinese supplies to the Canadian market, where an increasing weight is enjoyed by high value-added goods, including high-tech products, as well as products of the metalworking and chemical industries. The balance of Canadian-Chinese trade is traditionally positive for China. In the first months of 2022, there were noticeable changes in the dynamics and structure of Canada's foreign trade turnover with China. There is a drop in Canadian exports to China, in particular, a reduction in the supply of agricultural and mechanical engineering products. In general, Canadian-Chinese trade relations are becoming hostage to the intensification of confrontation between China and the United States, Canada's main military-political ally and foreign economic partner.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".