Bibliographic record
Abstract
An emergent priority in the field of transitional justice is gathering and analyzing empirical data to advance understanding of violent conflicts and responses to the transgressions committed during such events. A major segment of this research focuses on countries, policies, processes, and institutions as the units of observation. Among the limitations of such research, however, is the lack of direct, in-depth attention to relevant individual actors and their roles in these settings. Our article highlights a methodological approach that captures this perspective: surveys. Over recent years, scholars, NGOs, international organizations, and justice institutions have completed surveys of various scales with an assortment of populations, including those implicated in and/or exposed to violent conflict. Such surveys help to illuminate the circumstances and repercussions of conflict for individuals and their families and communities, their expectations about transitional justice, their assessments of contemplated and actual policies, processes and institutions, and the resulting impact on their attitudes, agency, and actions. In the process, these empirical data present a distinctive lens that we argue is integral to appreciating moral and pragmatic motivations for transitional justice, gauging responsiveness to the needs and interests of key constituencies, and evaluating consequences. We reflect on the merits, shortcomings, mechanics, challenges, and trade-offs of conducting surveys related to transitional justice in conflicted-affected societies. As part of the discussion, we cite examples of key studies from countries around the world, drawing on our own significant first-hand experience as well as research carried out by others.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.031 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".