Gender politics and geopolitics of international criminal law in Uganda
Bibliographic record
Abstract
This paper explores the views of victim survivors – both men and women – on the current prosecution of Dominic Ongwen at the International Criminal Court (ICC) for crimes against humanity, including the crime of forced marriage. This case will be used as the central story around which the potential and limitations of international criminal law for gender justice will be explored. The Ongwen case has blurred the lines between victims and perpetrators of child soldiering and has generated much debate within and outside the continent. It has resuscitated the contestation and controversies surrounding the ICC regime in Uganda and Africa more broadly. The reflections I share in this paper come out of a collaborative research project I direct called ‘Conjugal Slavery in War: Partnerships for the study of enslavement, marriage and masculinities’ (CSiW 2015-2020). While Uganda and the Ongwen case will be central to this paper, our research project includes partners working with survivors of conflicts in the Democratic Republic of Congo, Liberia, Sierra Leone, Rwanda and northern Nigeria. We collected well over 250 interviews with women who were abducted for forced marriage. Using interview data from Uganda, as well as court records, this paper explores in-depth the geopolitics and gender politics of prosecuting conjugal slavery as an international crime.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.026 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".