Contextualized Transitional Justice Policy Development in Uganda: Differentiating between Normativity Types in Evidence-Based Problem Analysis
Bibliographic record
Abstract
This article discusses the fundamental impact of normativity on producing evidence-based guidance for context-sensitive transitional justice policy. It draws on lessons learned from Uganda’s complex transitional justice context and extensive fieldwork to demonstrate the necessity and the means to differentiate between belief-based normativity and evidence-based normativity in conducting problem analysis as a crucial site that determines the integrity of evidence-based guidance. It also establishes that evidence-based normativity guiding problem analysis must include empirical evidence of societal dynamics and views of affected communities or there is a significantly higher risk of belief-based normativity decontextualizing strategy development. Findings establish significant substantive differences between the problem sets identified for intervention using a contextualized approach shaped by evidence-based normativity and those in Uganda’s National Transitional Justice Policy (NTJP), which was heavily influenced by belief-based liberal-legalist norms and standardized practice. Crucially, findings also show that the conventional mechanisms prioritized in the NTJP actively work against the mechanisms and aims affected communities prioritize for meaningful redress and to prevent recurrence of mass violence. The article offers concrete recommendations on how to evade belief-based normativity in academic and applied research models intended to produce evidence-based guidance for genocide and mass atrocities prevention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".