The Political Economy of China's Special Economic Zones and Adaptability of Developmental State Model of Industrialisation in Africa: Evidence From Nigeria
Bibliographic record
Abstract
ABSTRACT This study examines the structure and operations of China's Special Economic Zones (SEZs) in Nigeria and evaluates the zones' potential as catalysts for Nigeria's industrialisation. By using mixed qualitative methods, the result from fieldwork shows that SEZs are not likely to become the springboard for Nigeria's industrialisation. The study identifies the major challenges hindering the success of the SEZs in Africa and Nigeria, particularly which include the lack of adequate infrastructure and the inability of the state to formulate and effectively implement rules that would attract investors to the zones, engender technology transfers and generate other benefits for the local economy. The article concludes that even as there are lapses on the part of the Chinese collaborators concerning the speed of construction work at the zones, the performance of the Nigerian state reflects significant weaknesses. These weaknesses mirror the often‐cited dysfunctionalities in the political economy of Nigeria and other African states, where these aforementioned challenges weaken state capacities to initiate and implement development programmes. The paper suggests policy recommendations on how to make the SEZs effective instruments of industrialisation and economic growth, a fundamental aspect of which emphasises engendering a developmental state necessary for creating the conditions for growth.
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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 | 0.001 | 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 | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".