Feminism in Conversation: Campus Sexual Violence and the Negotiation Within
Bibliographic record
Abstract
This article traces the evolution of the feminist “sex wars” in contemporary debates about campus sexual violence reform in Canada and the United States – what Emily Bazelon calls the “return of the sex wars” at American colleges and universities. The return of the sex wars has been characterized by many of the same unproductive hostilities and painful acrimony as the original fight between feminist sex radicals and anti-pornography feminists over three decades ago. This article focuses on a particularly controversial issue in these debates: the role of consensual dispute resolution (i.e., negotiation, mediation, and restorative justice) in addressing campus sexual violence. Employing a two-person counter conversational methodology, the article stages a negotiation between two feminists with competing and representative views on this issue. Feminist concerns about consensual dispute resolution raise challenging questions about the rise of informal justice and its implications for the rule of law in campus sexual violence cases. The article concludes by arguing that the intense polarization and politicization of the return of the sex wars has led to a hollowing out of the feminist critical discourse in this area, which has prevented some feminists from engaging with consensual dispute resolution as a potentially viable and redemptive means of sexual regulation on campus.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.048 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| 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".