International Criminal Justice and the Empowerment or Disempowerment of Victims
Bibliographic record
Abstract
(NOTE: This is a pre-print of a chapter to be published in an edited volume tentativley titled Power in International Criminal Justice: Towards a Sociology of International Justice, from TOAEP). This chapter investigates victims' empowerment or disempowerment by international criminal justice processes Critical scholars often argue that these processes de-politicize, render passive, or otherwise disempower victims as agents of justice. Concerns about the powerlessness or disempowerment of victims are central to debates about whether international criminal justice does in fact advance ‘justice for victims.’ Based on focus group discussions and interviews with survivors of conflict and international crimes in Kenya and Uganda, I argue that neither the laudatory nor critical positions in this debate capture the very complex and variegated effects of international criminal justice processes on victims. Instead, tribunals are selective about who receives victim status, they channel people’s agency in particular ways, and their impact is highly context-dependent. As a result, victim status is not simply empowering or disempowering—it enhances the agency of some people in some contexts to pursue some justice aims, but it can also pose serious risks and constraints. This framework has implications for understanding the power of international criminal justice, and for evaluating the capacity of international criminal tribunals to advance justice for survivors of conflict and mass rights abuses.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".