Local Knowledges in International Peacebuilding: Acquisition, Filtering, and Systematic Bias
Bibliographic record
Abstract
Abstract There is widespread consensus among peacebuilding practitioners and scholars on the importance of integrating local knowledge into the design, planning, and implementation of international peace interventions. However, the concept of local knowledge remains undertheorized, and the dynamics of local knowledge integration in international activities have not yet been fully explored. This paper reconceptualizes “local knowledge” in peacebuilding as local knowledges in the plural, highlighting seven categories of relevant local knowledge and the contestation within each. We then draw on organizational theory to identify the processes by which particular types of local knowledge become more or less likely to be incorporated into internationally led peacebuilding activities. Specifically, we argue that knowledge incorporation consists of two stages: acquisition and filtering. In both, international actors control who is able to contribute knowledges and which knowledges are recognized. Systematic biases result: knowledges that confirm previously held beliefs or that simplify complexity are incorporated more regularly. We illustrate our argument by focusing on the UN, but suggest that our findings apply to other international actors, including non-governmental organizations, and extend beyond peacebuilding.
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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 | 0.042 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".