Problems of Local Participation and Collaboration with the UN in a Post-conflict Environment: Who Are the ‘Locals’?
Bibliographic record
Abstract
Current UN peace-building missions all share a strong emphasis on the importance of local participation to make peace processes sustainable. New missions are increasingly designed in order to favour local involvement in different projects. Yet, despite this insistence, there are significant problems that hinder collaboration between local and international actors. Through an analysis of interviews in the field with local actors and UN staff in Liberia and Burundi we identify and categorise the problems into two categories: actor and structure related. In an attempt to explain why these difficulties arise and persist, we use a sociological perspective that emphasises the importance of practical knowledge over representational knowledge in a post-conflict structure. We argue that both external and local actors' background knowledge and individual motivations, in combination with the many factors composing the constraining post-conflict structure, hinder an efficient collaboration, which could render the peace-building projects more sustainable. Structural suggestions on how to increase participation include more long-term projects, as well as longer mandates to avoid time pressure and to build capacity. Lastly, there is a need for detailed guidelines to understand who the ‘locals’ are, when to get involved and under what circumstances in order to avoid ‘spoilers’.
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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 | 0.025 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.022 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".