Countering the Wave of Democratic Backsliding and the Crisis of Democracy in West Africa
Bibliographic record
Abstract
While the early 1990s ushered in a period of democratic transition in African countries, the last decade has witnessed what many observers have called the process of democratic backsliding in many parts of West Africa. This article examines and analyses some of the main causes of democratic retrenchment in the West Africa sub-region and how to address them. The main argument in this article is that various constraints and challenges, such as the increasing return of the military in politics, electoral manipulations, constitutional reforms to benefit incumbents and the lack of economic dividends for citizens, are undermining the democratic progress initially made in the 1990s. Thus, addressing the challenges that lead to military intervention, including poor socioeconomic conditions, would go a long way in stemming the tide of democratic backsliding in West Africa. Additionally, ensuring increasing trust in institutions such as the judiciary, media, electoral management bodies and the electoral system, coupled with an increasing role for civil society organizations in the political environment, would help combat democratic backsliding in the sub-region.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".