Justice for Women After Sexual Assault: A Critical Interpretive Synthesis
Bibliographic record
Abstract
Justice after sexual assault is often understood and enacted through the criminal legal system such that the outcomes are binary (i.e., justice is achieved or not achieved). Previous research indicates that survivors have specific wants and needs following an assault in order to experience justice, which may or may not align with current practices. We conducted a critical interpretive synthesis of 5 databases to create a sampling frame of 4,203 records; the final analysis included 81 articles, book chapters, and policy documents. Results indicate that justice is an individualized and dynamic process which may include the experience of voice, connectedness, participating in a process, accountability, and prevention. The experiences of safety and control are central to each of these domains. Survivors may seek and enact these justice domains through several avenues, including the criminal justice and legal systems, restorative justice, medical/mental health spaces, activism, art, and social media. Existing actors within currently available justice systems, including legal, medical, and mental health personnel should encourage survivors to identify and define their own experience of justice, including locating helpful behaviors rooted in safety and control, and resist a binary model of justice. Extant systems should therefore be flexible and accessible to help survivors realize their preferred modes of justice.
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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 | 0.019 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".