“Die alone, old, and let the cat eat your face”: anti-feminist backlash and academic cyber-harassment
Bibliographic record
Abstract
Anti-feminist backlash has taken on a new form in the past decade with the rise of cyberattacks and the proliferation of Men’s Rights Activist groups, yet scant literature exists on the nature of cyber-harassment against feminist academics. This article uses the authors’ experience of cyber-harassment as a case study to explore the nature of online anti-feminist backlash against academic research. We identify three narrative forms of this backlash, which combines to create a “braided thread” of anti-feminist attacks. The academic setting presents a specific kind of cyber-hate that relies on the notion that progressive, critical researchers are “brainwashing” students. The attacks spread misinformation regarding methodological rigor in an effort to delegitimize and discredit feminist academics. Attackers also rely on tired claims of feminists as man-haters who seek to ruin men’s lives. Finally, anti-feminist backlash often resorts to instances of cyber-hate—ad hominem attacks that objectify women’s bodies and seek to humiliate and shame feminist scholars. These attacks are political and personal in nature, and spread misogynistic, white-supremacist, and heteronormative ideology in a vain attempt to silence feminist scholars.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.025 | 0.052 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".