The stories we tell victims: Victim participation and outreach programs at the extraordinary chambers in the courts of Cambodia
Bibliographic record
Abstract
Abstract Victim participation and elaborate outreach programs are becoming increasingly important features of international and hybrid criminal courts. The aspiration is that these programs will increase the local relevance of these courts' work, enhance the sustainability of justice processes after international actors leave, and empower victims en passant. In the last decade, considerable attention and resources have been dedicated to the development of state-of-the-art outreach and participation programs, resulting in exciting new engagement models that are then, on average, evaluated in legal and technical terms. So far, however, little attention has been paid to the more long-term and indirect effects of exposure to certain – unidimensional and hierarchically organized – narratives about justice. In a first step to systematically analyze this question, this article maps the discursive priorities and structure of one of the tribunals (Cambodia) where victim participation and outreach played a central role. The article uses innovative large-n qualitative computational text analysis methods and raises critical questions about (a) the disconnect between prosecutorial prioritization strategies and popular priorities for the justice process, (b) the success of outreach programs in bringing a locally relevant message, and (c) the extent to which outreach and victim participation succeeded in bridging the gap between the work of the ECCC and the realities of people on the ground.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.018 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".