Bibliographic record
Abstract
China is the second largest economy in the world and Canada’s second largest trading partner. According to the government of Canada, the two countries generally maintain a co-operative relationship. However, Canada’s economic co-operation with China is still limited; the share of Canadian exports to mainland China was only 1.08 per cent in 2014 and 4.11 per cent in 2015, while China supplied 12.26 per cent of total Canadian imports in 2015. Since 2012, Canada‘s exports to China have declined. Also, foreign investment between Canada and China is limited and experienced a downward trend from 2013-2015. There is a huge potential for Canada to expand its market share in China. Based on facts and accredited sources, this thesis intends to study whether Canada should further enhance its political and economic ties with China, and to identify what the opportunities, challenges and barriers are to Canada-China bilateral trade. The research here mainly covers foreign investment and the most promising trade sectors with high economic complementarities, including agriculture, clean technology and natural resources. The findings indicate that it is mutually beneficial for Canada and China to deepen their economic ties.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".