Institutionalizing Democracy in Africa: A Comment on the African Charter on Democracy, Elections and Governance
Bibliographic record
Abstract
Abstract This article provides an exegesis of the recently entered-into-force African Charter on Democracy, Elections and Governance. Democracy has a decidedly mixed history in Africa and, despite a concerted effort by the African Union (AU), it has made only halting inroads in those states that are nondemocratic or struggling to consolidate democracy. That may change as more states ratify and implement the Charter, a comprehensive regional attempt to promote, protect, and consolidate democracy that entered into force in February 2012. This Charter, the culmination of two decades of African thinking on how democracy should develop on the continent, represents the AU’s attempt to institutionalize principles of good governance and democratic ideals. Although hurdles remain on Africa’s road to democratic development, including poverty, illiteracy, and corruption, the Charter provides a means to address these stubborn problems. Whether it will succeed will depend on state implementation of the obligations undertaken by ratification of the Charter, as well as the AU’s own commitment to ensuring observation of the Charter’s key provisions. If the AU and its member states do fully implement and practically observe the Charter’s obligations, then the prospects for democratic governance in Africa have a bright future.
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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 | 0.036 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.032 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.034 | 0.049 |
| Insufficient payload (model declined to judge) | 0.004 | 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".