“Love on top of mercy”: complex decision making among women who survived wartime forced marriage in Northern Uganda
Bibliographic record
Abstract
Twenty years after the war (1986–2006) between the Lord’s Resistance Army rebels and Ugandan government ended in Uganda, girls who were abducted by the rebels still experience reintegration challenges. Forced to serve as fighters, laborers, wives and mothers to rebel commanders’ children, these women escaped abduction and returned to an impoverished community. While some women, upon escaping captivity, rejected the rebel commanders they were forced to act as wives to, several women remained in these relationships. Little information exists on women’s lives in the aftermath of violence, and even few studies have documented the complex decision-making and choices of women as they strive to rebuild their lives and support their children following the war. Using the frameworks of “love on top of mercy,” this article explores why some women elected to formalize and continue with relationships that began as wartime forced marrriages. Through the analysis of qualitative research documenting women’s voices and experiences, this article argues that women navigate numerous structural and systemic challenges, as well as experiences of discrimination and stigmatization, while engaging in strategic and agentic decision-making in relation to their decisions about who to marry or stay married to, and why. These findings highlight how documenting “agency within constraints” provides rich insights into complex decision-making as linked to survival, wellbeing and feminist flourishing.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".