People who run African affairs: staffing and recruitment in the African Union Commission
Bibliographic record
Abstract
Abstract This study contributes to the field of International Public Administration (IPA) and the emerging area of Informal International Relations (IIR) by examining the politics of staffing and recruitment of the African Union Commission (AUC). Although the AUC has become a major political player in international affairs, there is a dearth of knowledge about the civil servants who work for the AUC and who run this paramount pan-African executive body. To address the void, this paper draws on a survey of 137 AUC staff, archival studies and interviews to explore recruitment of AUC staff. Combining organisational theory and informality as analytical lenses, the study demonstrates that, first, many informal international practices (IIPs) are embedded in AUC recruitment processes. Second, the AUC is composed largely of short-term, contracted staff. Finally, it shows that the AUC is dependent on lower-ranked personnel or that it is bottom-heavy. Many of these lower-ranked officials are intimately involved in the making of AUC policies and decisions, putting into question the assumption in existing scholarship that decision-makers of IOs are primarily reliant on top-ranked A-level officials (senior management).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".