Resilience, Adaptive Peacebuilding and Transitional Justice in Post-Conflict Uganda: The Participatory Potential of Survivors’ Groups
Bibliographic record
Abstract
In the aftermath of the more than twenty-year armed conflict between the Lord’s Resistance Army and the Ugandan government, northern Uganda has become a transitional justice laboratory. In response to widespread human rights violations perpetrated by both the rebels and government soldiers, various peacebuilding and transitional justice mechanisms have been put into place. However, many of them are top-down and externally-driven, inaccessible to rural communities and/or irresponsive to diverse experiences and post-conflict needs. In this vacuum of post-conflict assistance, different alternative avenues have emerged at the micro level that ultimately enable war-affected communities to engage with their subjective experiences on their own terms. This chapter specifically focuses on the role of survivors’ support groups. It shows how different types of survivors’ groups, in a creative and participatory manner, enable survivors’ agency and craft spaces for healing, justice making and peace-building, shaped by survivors’ own experiences and needs. Support groups thereby aid survivors in developing adaptive capacities to positively respond to shocks and stressors resulting from mass violence. In this way, these groups also contribute to fostering individual and community resilience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".