Collective Learning at the Boundaries of Communities of Practice: Inclusive Policymaking at the World Bank
Bibliographic record
Abstract
This article explains the emergence of inclusive practices at the World Bank as a collective learning process between communities of practice. Contributing to the literature on practices and cognitive evolution in International Relations, this theory of learning goes beyond socialisation or meaning negotiation in communities in focusing on the translation of knowledge at the boundaries of communities of practice. This article also contributes to scholarship on international organisations in theorising communities and social processes that transcend formal boundaries. In brief, it develops three processes of change through collective learning (boundary encounters, brokerage, and the use of epistemic boundary objects) to understand the emergence of inclusive policymaking practices at the World Bank. Finally, it empirically explores how the Uganda Poverty Eradication Action Plan in 1997 participated in this collective learning. This research is based on 21 first-hand interviews, twenty publicly available interviews and extensive archival work.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.034 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.017 |
| 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".