Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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 it