Bibliographic record
Abstract
China's emergence as a global power is raising interest globally.This includes a rise in trade relations with Africa over the last two decades.China's relations with Africa have undergone several changes since the onset of modern China-Africa relations in 1955.In the current phase, relations have been marked by increasing economic interdependence.During the same period, China has signed Free Trade Agreements (FTAs) with many countries in different continents and African countries have also embarked on their own FTAs.However, no FTAs have yet been signed between China and any country in Africa.This thesis examines the question: What are the Prospects of China Signing FTAs in Africa?This thesis documents the growth of China-Africa trade over the last two decades, analyses the reasons why China signs FTAs, and assesses whether three African countries, Nigeria, South Africa and Sudan meet the criteria for China's FTA partners.The thesis finds that there is a slim chance that China will sign FTAs with any of these three African countries in the near term, because African countries are at the fringe of China's foreign policy.African issues are rarely discussed at the highest decision-making authority in China, and China's FTAs with African countries may have impacts on the latter's manufacturing sectors.Over the medium-longer term, South Africa is probably the most likely candidate of the three countries for a FTA with China.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".