Before #MeToo: Violence against Women Social Media Work, Bystander Intervention, and Social Change
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it