China’s special economic zones and the industrialisation of Africa
Bibliographic record
Abstract
Africa’s interest in the Chinese experience of industrialisation has led to the development of SEZs on the continent. The past two decades have witnessed an evolution of Africa from relying on traditional industrial zones to implementing numerous modern international SEZs, and from having only seven China–Africa Economic and Trade Cooperation Zones to possessing modern African SEZs of various developers and operators. The African SEZs inspired by China are increasingly widespread. As a new strive for development, African industrialisation through SEZs is characterised by underlining export promotion and the preference for the labour-intensive manufacturing industry based on well-developed infrastructure and an active role of the state. China has been supporting African SEZs in the whole process from financing, construction, and management to industrial transfer and investments. Despite the significant opportunities that SEZs offer, African countries still face several limitations in implementing them successfully. African policy-makers and other actors involved in the SEZs can better adapt to the new approach of industrialisation by making relevant adjustments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".