Feminist Strategies Against Digital Violence: Embodying and Politicizing the Internet
Bibliographic record
Abstract
This article aims to analyze feminist strategies against digital violence and their relation to performative forms of social justice. Based on new feminist materialisms (Coole & Frost, 2010; Souza, 2019), the article shows how female bodies are at the crossroads in our digital society. On the one hand, they are a target of digital violence because of their political activities, while on the other hand feminist protesters are opening new political possibilities for mobilization. By conducting a digital ethnography with two social collectives located in Mexico City – Luchadoras and Laboratorio de Interconectividades – I argue that embodying and politicizing technologies are strategies to mobilize the body as a political, critical and material category, which reveals a renewed feminist agency in hacking the hegemonic meanings of digital technology and resignifying its materiality in order to politicize it. Furthermore, I argue that based on the body as material category, innovative forms of social justice for feminist collectives emerge. These strategies are related to the critical questioning of technologies to repoliticize digital violence, to render visible the memories and affectations in women’s bodies, as well as to mobilize a new feminist positioning called hackfeminist self-defense. All in all, this article seeks to contribute to understanding the broader issue of feminist politics performing social justice in the digital era.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".