Towards better GVC participation in Africa: Does infrastructural development matter?
Bibliographic record
Abstract
Global Value Chain (GVCs) offer vast opportunities for industrialization, economic diversification, and structural economic transformation. However, despite various efforts to become notable actors in the GVC, many African countries remain positioned at the lower end of the GVC spectrum. Hence, this paper investigates the potential of infrastructure to catalyse GVC participation and elevate African countries to a level comparable to developed nations. Relevant data from 38 African countries spanning 2005–2021 are analysed using the Pooled Ordinary Least Square (POLS), and the System Generalised Method of Moments (SGMM) methods. The results provide overwhelming evidence of infrastructural development's crucial role in improving forward and backward GVC participation in Africa. Specifically, information and communication technology (ICT), electricity, and water infrastructure significantly promote forward and backward GVC participation. The results are robust to alternative analytical methods. African governments, therefore, need to pursue pro-infrastructural development policies and increase infrastructural spending to improve GVC participation.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".