Bibliographic record
Abstract
Borrowing from Joanna Bourke, in this book the central facet of what constitutes sexual violence is whether a person identifies what happened to them as a negative experience that was sexual in nature and unwanted, coerced, or not consensual, however they want to define those terms. 1Therefore, if someone -either in the news, case law, or my researchidentified their experience as sexual violence, I accepted their claim.This definition of sexual violence does not claim normative status, nor does it claim to be "truth"; rather, there is neutrality regarding the veracity of any claim. 2Such an approach allows us to problematize a particular element of the issue: defamation lawsuits that follow the disclosure or report of sexual violence.The intention here is not to examine or make a judgment about the truth of the claim; the purpose is to examine the consequences of litigation, or the threat of litigation, for making statements about sexual violence.Every man named in this book has denied the allegations of sexual and gendered violence made against him.A majority have resorted to legal action to vindicate their reputations, with varying degrees of success.This book is not about any single individual; it is a systematic examination of defamation law and the institutional structures that contribute to the silencing of sexual violence discourse.Canadian defamation laws, as this book demonstrates, do little, if anything, to protect those who speak publicly about sexual violence.
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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 | 0.007 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.025 | 0.023 |
| Insufficient payload (model declined to judge) | 0.076 | 0.094 |
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".