African Continental Free Trade Area Agreement – Does the Facts Support the Benefits for Nigeria?
Bibliographic record
Abstract
Hesitantly, but finally, Nigeria joined the African Continental Free Trade Area (AfCFTA) with the Nigerian President, Mohammadu Buhari, signing the protocol at the African Union Summit in Niamey on July 7, 2019 based on perceived benefits. This study interrogated the purported benefits for Nigeria using standard trade costs between Nigeria and peer countries in Africa. Using a content analytical framework on a dataset by World Development Indicators and World Integrated Trade Solutions, the study found that average tariff rate in Nigeria is very high when compared to that of her major trading rivals in Africa like Ghana, Egypt and South Africa. Furthermore, the study found Nigeria in a comparative disadvantaged position on the ease of doing business in the same setting. Also, Nigeria’s major export commodity is crude oil and lubricants which has little or no market in the continent. Besides, trade-related infrastructure, especially roads and maritime corridors, in Nigeria is poor even by African standards. With these structural problems, ipso facto, Nigeria may not benefit maximally and comparatively in the enlarged continental market envisioned by the AfCFTA agreement. The study therefore, recommended that Nigerian government should continue to maintain the present cautious approach and refrain from making further commitments on the AfCFTA deal. In the meantime, the country should embark on massive infrastructural and trade-related development, improve the ease of doing business and diversify the economy in order to be in vintage position to exploit the potential opportunities offered by the AfCFTA in the medium-to-long term horizon.
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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 | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".