Bibliographic record
Abstract
Abstract Pan-Africanism and references to a shared African cultural identity have an important function in the way the African Union (AU) seeks to mobilize a sense of belonging among African citizens. However, we know very little about how African citizens, in turn, relate to and identify with the AU and what shapes their sense of belonging as political subjects of the AU. In addressing this lacuna, this article takes a bottom-up perspective on the formation of an AU identity among African citizens, placing citizens’ own sense-making practices about the relevance and value of the AU in their everyday lives center stage. Drawing on focus group discussions among citizens in Burkina Faso and The Gambia, I show that the way research participants relate to the AU is based on and mediated through experiences. Rather than a vague Pan-African identity, what shapes the way citizens relate to the AU are concrete experiences with the organization’s norms and policies and their tangible effects on everyday life, which are conditioned by people’s (different) exposure to AU policies and their positioning within existing social, political, and economic structures. The importance of experience in forging a sense of belonging among African citizens does not preclude the existence of a shared Pan-African identity, but it offers important cues for both how to study the formation of an AU identity and how it can be shaped in the 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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.049 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| 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".