An Investigation of Economic Sanctions and Its Implications for Africa
Bibliographic record
Abstract
Economic sanctions are not only applied to countries in Africa by the United Nations (UN), the European Union (EU) and the United States (US) but also by the African Union (AU) and the Economic Community of West African States (ECOWAS) as well. The African continent is considered to be the most affected in terms of the influences of more economic sanctions from the UN, EU, and the U.S than any other continent across the globe and these sanctions normally comes into force as a result of conflicts, civil wars and also unconstitutional overthrow of a constitutionally elected government. Also these sanctions come to serve as a punishment and a deterrent to those who deviate from or go against internationally agreed laws.Undeniably, in recent years economic sanctions have become more effective and an efficient known foreign policy tool used as the number one alternative to halt wars or military takeovers.Despite economic sanctions being widely accepted by the international community as the most effective panacea and also a preferred choice, when it is imposed on a state, it has serious repercussions on the innocent citizens while the initiators or the main officials in various positions for whom these sanctions were intended for are always left off the hook.This paper therefore investigates the merits and the demerits that are associated with economic sanctions both within some countries on the African continent and the non-African continent. In addition, we will elaborate on the implications of such sanctions relative to the Africa Continent. The paper is divided into four sections. The first section of this paper elaborates on the introduction, the importance of economic sanctions and the types of sanctions. The second section deals with the definition of economic sanction, explains the sanction process at EU, AU, UN and the US and the final part looks at both the positive and negative effects of economic sanctions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".