Indicators and Success Stories: The UN Sustaining Peace Agenda, Bureaucratic Power, and Knowledge Production in Post-War Settings
Bibliographic record
Abstract
Abstract Most discussions on knowledge production in peacebuilding and conflict management have focused on the study of epistemic communities and strategic coalitions of global and local actors. This article shifts the focus away from who produces knowledge to the underexplored question of how knowledge is generated, repackaged, deployed, or ignored. Combining sociology of knowledge approaches with feminist governmentality scholarship, I critically interrogate the role of reports as knowledge production artifacts and report writing as bureaucratic practices that serve to design and implement UN Peacebuilding Fund (PBF) projects on Sustaining Peace. Specifically, I analyze the role of reports and reporting in four PBF projects on gender and reconciliation in Liberia, and I show how through the mechanisms of persuasion and homogenization, reports serve not only to measure success and failure and to produce contextualized knowledge, but also to exert symbolic power, (re)producing authoritative knowledge on women, gender and reconciliation, and giving legitimacy to external interventions. Studying how knowledge is produced instead of who produces it enables us to apprehend the entanglement of the local and the global and overcome simplistic binaries and oppositions, all while paying attention to how the production of knowledge, and its silences, remains embedded in global power relations.
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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 | 0.023 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.011 | 0.036 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 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".