Bibliographic record
Abstract
The challenges of protecting human rights after violent conflict are daunting. Often the very state institutions charged with protecting and promoting rights are the same ones that perpetrated crimes during the war, or must accommodate disarmed groups within new governance structures to move toward peace. Critical institutions to protect rights – the judiciary, the police, schools, hospitals and social welfare institutions – may have been destroyed and the economy and national treasury necessary to reconstruct them are empty. The rule of law exists mainly as an idea, and must be reconstructed to build social trust. Communities, families, have endured and fractured under the strain of violence, and must learn to live together again after so much loss. How is a survivor of mass violations of human rights to rebuild her life in such circumstances? How does her situated knowledge, as one who navigates such a complex and fraught terrain, provide further insights into the possibilities of realizing human rights after conflict? Northern Uganda is the site of a decades-old conflict (1987–2008) between the Ugandan government and the Lord’s Resistance Army (LRA). During this war – which now continues in the Democratic Republic of Congo, South Sudan and the Central African Republic – the LRA abducted over 60,000 children and youths, forcing them to fight, act as porters and become wives to commanders (Blattman and Annan 2010). The Ugandan government pursued multiple military strategies to crush the rebels, one of which was to forcibly displace up to 90 percent of the population into poorly protected camps (Dolan 2009). Both the LRA and Ugandan People’s Defence Forces (UPDF) are responsible for inflicting grave atrocities on the civilian population, including rape, murder, lootings, arson, torture, beatings, forced enslavement and humiliation (Amnesty International 1999; Branch 2011; Human Rights Watch 2003).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".