Exploring Communities of Practice from an Informality Perspective: Insights from the AU, ECOWAS, and UN in West African Mediation Theaters
Bibliographic record
Abstract
Abstract The article explores how and why communities of practice (CoPs) of international organizations (IOs) work together effectively despite the rigid formal bureaucratic and institutional borders they inhabit. The manuscript explains how four informal mechanisms combined to enable CoPs embedded in the African Union (AU), the Economic Community of West African States (ECOWAS), and the United Nations (UN) Organization to resolve political crises in Burkina Faso, Gambia, Ghana, Guinea, Mali, and Togo between 2016 and 2022. The informal mechanisms allowed CoPs to overcome their institutional limitations, cross rigid organizational borders, and work together to resolve different political crises in the six countries. Some of the informal mechanisms were cultivated by CoPs, while others emerged organically from activities and interactions of these like-minded professionals. The informal instruments that were developed and used to resolve the crises provide a telling illustration of how CoPs create global governance norms, practices, processes, rules, and structures from below. The enabling role that informality played in the six conflict theaters suggests that paying close attention to the informal dimensions of CoPs has enormous analytical benefits.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.021 | 0.043 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.013 |
| 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".