Africa's international trade paradox, technology transfer, and value chain upgrade
Bibliographic record
Abstract
This study examines the paradoxes in trade and global value chain (GVC) in Africa. Firstly, we analyzed international trade statistics of selected countries to highlight paradoxes in resource endowments, exports, and imports. We found contrasts between Africa's development potential and its current reality in trade and value capture. Secondly, we analyzed the content of reports focused on GVC challenges in four countries. We found that the reports predominantly focused on identifying issues in GVC upgrading in Africa rather than proposing solutions. These challenges, faced by developing economies in critical sectors, were categorized across four dimensions identified in the literature: technological capabilities, firms, government intervention, and joint ventures (JVs). Thirdly, we analyzed how to address these GVC upgrade challenges by using interviews from successful GVC upgrade cases in two additional countries. We applied four theoretical dimensions to categorize actions that successfully facilitated GVC upgrading. We found that of all the success factors, government intervention—acting as a shareholder, producer, regulator, and negotiator—was the most critical for successful GVC upgrading. Actions by the state, such as a well-organized early upgrade plan, coercive local expectations toward foreign firms, a winning negotiation approach, and concise JV agreements, were instrumental in these successes. • International trade paradoxes persist in Africa, keeping nations underdeveloped • GVC upgrade in Africa faces issues with technology, firm, state, joint venture • Government intervention is the key to GVC upgrade and trade paradox reversal • Early plan for GVC upgrade facilitates technological upgrade and value capture • Joint ventures play an important role in technology transfer toward GVC upgrade
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".