Lost in Translation? Agency and Incommensurability in the Transnational Travelling of Discourses of Sexualized Harm
Bibliographic record
Abstract
This article argues for incommensurability, incoherence, and difference as the grounds through which to think about sexualized harm and its redress. It seeks to remove the “me” from the “too”, and to instead consider the structures of white supremacy and neocolonial power that have facilitated white Western feminists’ ability to participate in shaping a hegemonic discourse of sexualized harm and its transnational travelling. The article traces the author’s personal genealogy of rights work in the context of shifts in international jurisprudence in relation to wartime sexualized violence. It looks back and reflects on an eight-year feminist participatory action research project that accompanied 54 Mayan women protagonists who survived a multiplicity of harm, including sexual violence, during Guatemala’s 36-year genocidal war. The project documented the protagonists’ engagement with transitional justice mechanisms, including a paradigmatic court case and a national reparations program, as part of their struggles for redress. The concept of “protagonism” is used to understand agency in the aftermath of genocidal violence as relational, co-constructed, and imbued with power. The meaning of sexualized harm is always “in-translation” between Western and Mayan onto-epistemological positionings, as Mayan women seek to suture land-body-territory in their multifaceted strategies for redress that engage but always exceed rights regimes.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.071 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".