Pain Generation: Social Media, Feminist Activism, and the Neoliberal Selfie
Bibliographic record
Abstract
Explores the perils and promise of feminist social media activismSocial media has become the front-and-center arena for feminist activism. Responding to and enacting the political potential of pain inflicted in acts of sexual harassment, violence, and abuse, Asian American and Asian Canadian feminist icons such as rupi kaur, Margaret Cho, and Mia Matsumiya have turned to social media to share their stories with the world. But how does such activism reconcile with the platforms on which it is being cultivated, when its radical messaging is at total odds with the neoliberal logic governing social media?Pain Generation troubles this phenomenon by articulating a "neoliberal self(ie) gaze" through which these feminist activistssee and storify the self on social media as "good" neoliberal subjects who are appealing, inspiring, and entertaining. This book offers a fresh perspective on feminist activism by demonstrating how the problematic neoliberal logic governing digital spaces like Instagram and Twitter limits the possibilities of how one might use social media for feminist activism
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".