Introduction: The African Union, Pan-Africanism, and the Liberal World (Dis)Order
Bibliographic record
Abstract
Abstract Long considered peripheral to both international affairs and the discipline of international relations (IR), Africa is increasingly at the center of global politics and academic debates. Global powers are competing for economic, political, and strategic influence on the continent, while Africa itself has emerged as an increasingly powerful and confident actor on the world stage. In large part, this is due to the leadership of the African Union (AU), which since its founding twenty years ago has embarked on an ambitious agenda inspired by Pan-Africanism, seeking to create an Africa that is a “Strong, United, and Influential Global Player.” Following the AU’s twentieth anniversary, this article and Special Forum situate the AU within recent debates in IR about non-Western agency and the contributions of the global South to world politics. Focusing on the role of the AU and Pan-African ideology in shaping Africa and its international engagements, we argue that an analysis of the AU and the influence of Pan-Africanism is crucial to an understanding of Africa’s actions and positions in contemporary world affairs. We conclude that the heightened geopolitical rivalry following Russia’s invasion of Ukraine threatens to undermine two key aspects of the AU’s Pan-Africanism, namely its commitment to democracy and human rights and its ambition to speak with a united voice on the world stage.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".