Justice and the Politics of Peace Building: Comparing Experiences in Kosovo, Cambodia and Northern Uganda
Bibliographic record
Abstract
What ‘justice’ means, and how or where different forms of justice fit within larger processes of conflict resolution and sustainable peace - such as war-to-peace transitions, ceasefires, peace settlements and post-conflict peace building - are questions that defy simple answers. Peace and justice too often have become idealized or politicized notions, sometimes portrayed as intimately and positively intertwined (no peace without justice), and on other occasions declared as mutually contradictory (no peace settlement without withdrawal of International Criminal Court (ICC) indictments). Serbia/Kosovo, Cambodia and Uganda provide three fascinating case studies of the complex political debates that are attached to the ideas of justice and peace building. In each case, internal (local, national) and external (regional, international) political, social, economic and other influences have played roles in shaping the nature of the ‘justice’ that is sought by various actors in the violent conflicts that have done so much harm to their populations. What emerges from the analysis here is a story not of a single, clear path towards justice, reconciliation and sustainable peace, but rather of a difficult, awkward and uncertain process of balancing goals and claims that at different times can be complementary or contradictory, central or irrelevant, or more often a mixture of values that can change over time and circumstance as well as in the eyes of the beholder.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| 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".