Book Review: Disclosing Sexual Violence in a Digital Society: Storytelling, Activism, and Justice O’NeillTully, Disclosing Sexual Violence in a Digital Society: Storytelling, Activism, and Justice. Crime and Justice in Digital Society. Cham: Springer, 2024, 160 p.: ISBN 9783031749896 (Hardcover); $189.95, ISBN 9783031749902 (eBook); $178.09.
Bibliographic record
Abstract
Tully O'Neill's Disclosing Sexual Violence in a Digital Society is an interdisciplinary academic work that spans the fields of digital criminology, gender studies, sociology, and critical theory.Grounded in the lived experiences of survivors of sexual violence in the digital age, the book explores how these survivors use social media, forums, blogs, and other digital platforms to engage in self-disclosure, mutual support, and the pursuit of justice.Drawing on extensive qualitative interviews, online content analyses, and critical theoretical constructions, O'Neill challenges the traditional criminal justice system's approach to sexual violence cases and proposes a multifaceted understanding of "informal justice" in digital contexts.The book not only offers new theoretical perspectives for research on sexual violence and digital sociology but also provokes deep reflections on modern practices of justice and the transformation of social structures.The book is organized into two parts (Speaking out online and Rape Justice in Digital Society), each containing four chapters.It centers on three core questions.First, how do victim-survivors disclosure practices on digital platforms reconstruct their self-identities and foster community connections?Second, to what extent do these disclosure behaviors constitute "informal justice," and how does this pursuit of justice contrast with traditional, institutionalized justice systems?Finally, how do neoliberalism, trauma narratives, and discourses of vengeance collectively influence and constrain survivors' self-expression and the realization of justice in digital environments?O'Neill first provides a detailed account of how victim-survivors select different disclosure strategies on various digital platforms such as Reddit, Facebook, and Twitter/ X.According to O'Neill, "speaking out" (public disclosure) and "speaking in" (private communication) are not mutually exclusive.Instead, they form a continuum, with choices often depending on one's perceptions of privacy, safety, and intended audience.O'Neill terms this disclosure process "safety work," emphasizing that in the digital realm survivors must continually balance the tension between information sharing and personal protection, guided by platform characteristics, audience expectations, and individual emotional needs.In terms of the pursuit of justice, O'Neill introduces the concept of "informal justice," arguing that in the digital age, the very acts of self-narration, online mutual support, and collective witnessing by survivors inherently embody a form of ORCID iD
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 0.019 |
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".