Defamation as a Sword: The Weaponization of Civil Liability against Sexual Assault Survivors in the Post-#MeToo Era
Bibliographic record
Abstract
When the #MeToo movement gained popularity in 2017, the impact that it would have on how societies perceive sexual violence against women was unpredictable. In the midst of the female empowerment and support that the hashtag cultivated, a legal phenomenon was brewing in the form of retaliatory defamation lawsuits from men accused in this modern wave of sexual assault allegations. This analysis features a step-by-step breakdown of the life of a defamation lawsuit filed against a sexual assault survivor making an online sexual assault disclosure and explores this increasingly popular intimidation tactic. In doing so, I illustrate the way in which Canadian defamation law, though well suited to its predetermined purpose, is wholly inappropriate when applied to a #MeToo context, where it essentially becomes used to litigate sexual assault claims in a manner that disadvantages survivors and inadvertently reinforces rape myths in the legal analysis.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.024 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".