Transformative Gender Justice : Constructions of Justice, Violence, and Reparations for Survivors of Conflict-Related Sexual Violence in Northeastern Nigeria
Bibliographic record
Abstract
Alongside global acknowledgements of the gendered nature of conflict, the academic field of transitional justice is shifting towards the newly emerging concept of transformative justice. Within this, reparations are particularly seen as the most agent-centric tool for structural transformation. In northeastern Nigeria, the conflict between Boko Haram and the state has seen a prevalence of conflict-related sexual violence, but survivors have struggled to claim their right to reparations. In this context, the Global Survivors Fund has established an interim project to ensure survivors have access to reparations. Through qualitative interviews with project staff and partners, this case study aims to explore how concepts of justice and transformation are constructed in the local context, and uses a gender lens as central in the analysis of these constructions. Principal findings were that justice for survivors is constructed within the rigid gender hierarchy present in the Northeast, and that gendered stigma manifests itself differently, with females subjected to societal and economic ostracization, and males unable to identify as survivors due to the risks to their masculine identity. The provision of greater agency, education, and reduced community stigma around survivorship through reparations were constructed as transformational aspects of the Global Survivors Fund’s project.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.019 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".