Bibliographic record
Abstract
How do activists in Latin America fight for change both online and in the streets? This piece narrates a course on Feminist Protest and Performance in Latin America that explores the limits and possibilities of feminist activism in physical and digital spaces. At this critical historical juncture, feminists across the hemisphere are organizing en masse to demand change and justice, to denounce pervasive misogyny and gender violence, and to envision and realize another world. Drawing on a long history of struggle, they are engaging in performance artivism across multiple platforms including Las Tesis piece El Violador Eres Tu (The rapist is you), under the hashtags #NiUnaMenos (#NotOneWomanLess) and #AbortoLegalYa (#LegalizeAbortionNow), and in massive physical occupations and protests like #OcupaEscola (#OccupyTheSchools). They are mobilizing to condemn femicide, to advocate access to legal abortions in public hospitals, and to introduce comprehensive sex education in public schools. Drawing on these interconnected forms of performance and protest, what Marcela Fuentes refers to as “performance constellations,” women and disidencias sexuales are fighting together for the right to live without fear, to make decisions about their own bodies, and to exist in a more just world. This class asks students to learn from Latin American feminst movements and to connect their insights to our intimate and collective experiences. Beyond the syllabus, this piece offers reflections on the philosophy of co-teaching, transnational activism across the Americas, and modes of embodiment that can happen online. We invite students and educators alike to consider what it might mean to “perform well” in a university class focused on pleasure and solidarity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".