Before #MeToo: Violence against Women Social Media Work, Bystander Intervention, and Social Change
Bibliographic record
Abstract
High-profile, social-media-fueled movements such as #MeToo have captured broader public attention in recent years and sparked widespread discussion of violence against women (VAW). However, online prevention work was underway in the years leading up to #MeToo, as the emergence and proliferation of social media enabled individuals to be increasingly active participants in shaping conversations about VAW. Situated within feminist VAW scholarship and the social–ecological framework of violence prevention, this paper draws from interviews with a cross-section of service providers, public educators, activists, advocates, writers, and researchers to analyze “conversation” as a central theme in VAW prevention work in social media. Results reveal that these conversations take place in three central ways: (1) engaging wider audiences in conversations to raise awareness about VAW; (2) narrative shifts challenging societal norms that support or enable VAW; and (3) mobilization around high-profile news stories. The paper finds that, through these conversations, this work moves beyond individual-level risk factors to target much needed community- and societal-level aspects, primarily harmful social norms that circulate and become reinforced in digital media spaces. Moreover, while bystander intervention has traditionally been approached as an offline pursuit to intervene in face-to-face situations of VAW, this paper argues that we can understand and value these VAW prevention efforts as an online form of bystander intervention. Finally, resource challenges and VAW prevention workers’ experiences of harassment and abuse related to their online work highlights a need to strengthen social and institutional supports for this work.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".