Facilitating Expansion of African International Trade through Information and Communication Technologies
Bibliographic record
Abstract
Abstract This article observes that expansion of international trade, particularly exports of appropriate goods, by African economies is important to their growth and development. Unfortunately, Africa’s share of world trade has been decreasing rather than increase. The continent is behind other continents in developmental terms, despite its many resource endowments. Governance deficiencies (broadly defined) are a major cause of the inability of African economies to manage their resources for sustained growth and development. The article looks at the particular consequences of governance deficiencies on trade expansion and goes on to suggest that the deployment of ICT can ameliorate those deficiencies. It focuses on the potential positive impact of e-governance in general, and e-customs in particular, on trade expansion. It argues that e-governance, including e-customs, has the potential to enhance the international competitiveness of African economies, increase revenues for government, and increase FDI inflows for production and exportation. It concludes that while e-customs is not a panacea for Africa’s international trade under-performance, it is an important piece of infrastructure that is relatively easier and cheaper to build, but which has a high beneficial impact on trade expansion. It therefore recommends the implementation of efficient e-customs in African economies that do not yet have such systems.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".