Clarity, Coherence and Context - Three Priorities for Sustainable Peacebuilding
Bibliographic record
Abstract
This paper will focus on three challenges that should inform the 2010 Review of the UN Peacebuilding Commission, namely: (1) developing the UN peacebuilding concept and operational model; (2) significantly stepping-up efforts to improve system-wide coherence; and (3) seriously implementing the principle of local ownership. There is a need to revisit and clarify exactly what it is the UN understands with the peacebuilding concept, and the Peacebuilding Commission is ideally suited to be the forum where such a debate should take place. Our peacebuilding efforts are challenged by deep-rooted coherence dilemmas.Instead of glossing over these dilemmas, the Peacebuilding Commission should be a strategic marketplace where these dilemmas can be debated and managed. In order to improve the sustainability of peace operations the UN Peacebuilding Commission will need to focus on three critical areas, namely local ownership, local context and local capabilities.Peacebuilding that focus only on building the executive branch of the state is not a good recipe for a sustainable peace.Investment in the immediate aftermath of a conflict should be primarily focused on developing capacity so that societies are empowered to manage their own downstream development.Local social capital and inherent capacities should be identified, recognised and used as the basis for further development.
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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 | 0.039 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.020 | 0.064 |
| Scholarly communication | 0.024 | 0.021 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.006 | 0.010 |
| 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".