La performance “Un violador en tu camino” de LASTESIS como denuncia al femigenocidio: articulaciones entre los casos chileno, argentino y diáspora latinoamericana en Auckland
Bibliographic record
Abstract
Durante el período de pandemia (COVID-19), oficialmente reconocido en marzo 2020, hubo un aumento de casos de violencia de género en países como Chile y Argentina. Sólo unos meses antes, en noviembre 2019, el colectivo chileno LASTESIS había salido al espacio público precisamente para denunciar la violencia en contra de las mujeres, ello con su “Un violador en tu camino”. Como acto artístico-político, la performance permitió elucidar y divulgar tanto a nivel local como internacional la teorización feminista de carácter decolonial, estableciendo un diálogo directo, por ejemplo, con la obra de Rita Segato. Mediante un proceso de retroalimentación, la propuesta se ha transformado en un himno de empoderamiento de carácter social e inclusivo. Inscripto en una perspectiva feminista-decolonial, este artículo contextualiza la performance y examina las réplicas del Movimiento de Sordes Feministas Argentina (MOSFA), de la Colectiva NiUnaMenos Tilcara-Maimara de Jujuy (Argentina) y de la diáspora latinoamericana en Auckland (Nueva Zelanda). Se sostiene que, al irse corporeizando como un nuevo espacio de resistencia feminista, decolonial y contestario de la violencia de género, la performance denuncia el feminicidio a nivel local e internacional, visualizando y problematizando el femigenocio.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".