Between the Duty to Remember, Make Reparation, and Decolonial Feminism: How Can We Combat Gender-Based Sexual Violence?
Bibliographic record
Abstract
Gender-Based Sexual Violence (GBSV) refers to coercive sexual acts regardless of the victim's relationship with the perpetrator or the setting. Prevalent during armed conflicts, it not only directly harms women but also impacts entire communities. Survivors face physical, mental, and social repercussions, compounded by societal shaming. Rooted in power dynamics influenced by colonization, GBSV perpetuates societal norms and prejudices. Traditional practices like male initiation rites contribute to a culture of male dominance, exacerbating GBSV. Storytelling plays a therapeutic role, but communities may perpetuate stigma, hindering support access. Addressing these obstacles, a holistic healing model, initially implemented at Panzi Foundation, provides avenues for survivors’ reparation and empowerment through medical treatment, psychosocial support, legal services and socioeconomic reintegration. Hence, the role of communities and governments is crucial. The duty to remember and repair presents challenges for states, especially in the context of war. Governments’ memory shaping raises politicization and reconciliation queries. The United Nations recognizes norms of amendment; yet achieving genuine reparation requires addressing root causes and consequences comprehensively. This leads to embrace African solutions, involving drawing from ancestral heritage and transcending colonial borders. Decolonial feminism focuses on demilitarizing society, which entails civil society's active engagement. Postcolonial feminism challenges patriarchal structures and promotes gender equality, addressing intersecting violence. Amidst calls for forgetfulness, commemoration and accountability, a just and faithful memory approach is advocated to conquer peace.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.031 |
| Scholarly communication | 0.008 | 0.019 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".