The behavioural logics of international public servants: the case of African Union Commission staff
Bibliographic record
Abstract
Although international organisations (IOs) are created by governments, their international public administrations (IPAs) have succeeded in ring-fencing their resources, and policymaking from direct intervention by member states. Research shows that international civil servants are best able to protect their autonomy when embedded in large and well-resourced IPAs. Staff in large IOs use their huge size, bureaucratic complexities, and different behavioural logics to protect their autonomy and thereby leave a ‘bureaucratic footprint’ in international affairs. Whereas the behavioural logics of large IPAs, mostly headquartered in the Global North, are reasonably well-documented, not much has been written on behavioural logics of international civil servants embedded in small secretariats. We seek to address the gap using the African Union Commission (AUC) staff. Drawing insights from organisational theory and mixed research methods, including the first ever comprehensive survey of AUC staff, the study finds that the AUC staff primarily evoke a departmental behavioural logic. In the absence of departmental logics, the preference of AUC staff is to take on supranational, transnational, and lastly intergovernmental persona. The reluctance of AUC staff to evoke intergovernmental logic is surprising given that the AUC is embedded in an intergovernmental governance architecture.
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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 | 0.024 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.027 | 0.038 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".