Looking beyond conflict: the long-term impact of suffering war crimes on recovery in post-conflict northern Uganda
Bibliographic record
Abstract
This thesis studies the experiences of alleged war crimes during the armed conflict in northern Uganda (Acholi and Lango sub-regions) and the multiple challenges these experiences present to youth attempting to recover in the post-conflict period. The thesis draws on primary quantitative and qualitative data collected in Acholi and Lango sub-regions in northern Uganda between January 2013 and December 2017. The findings show that youth who experienced or witnessed war crimes, especially those who suffered multiple war crimes, find it hard to regain lost education and experience more challenges maintaining good relations with their families and society in the post-conflict period. Similarly, strict gendered patriarchal norms and expectations render it challenging for conflict-affected youth to reintegrate into their families and society, particularly for women survivors of wartime sexual violence and their children born of war. The finding challenges the idea that ‘recovery’ is linear or that the end of conflict ‘normalises’ experiences of war crimes. Additionally, whereas war crimes suffered during conflict do impact livelihoods and recovery of young people, broader social, cultural, economic, and political processes also greatly matter. Lastly, while the conflict heightened individual vulnerability and complicated the recovery process, these factors do not entirely erase young people’s agency. Some young people were able to effectively and positively maneuver even withn the limitations of their circumstances.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".