The Predictors, Motivations and Characteristics of Image-Based Sexual Abuse: A Scoping Review
Bibliographic record
Abstract
Image-based sexual abuse (IBSA) is a form of sexual violence and abuse that is facilitated by the use of technology. The array of different technologies, ever-changing behaviors, and varied terminology have created challenges in terms of appropriate response, legislation, and the protection of victims as well as difficulties in establishing the extent and harms of this behavior on a wider scale and context. This scoping review examines and synthesizes the current literature which focuses on predictors, the motivation of perpetrators, and the characteristics of both victims and perpetrators in relation to IBSA. The databases Web of Science , ASSIA , ProQuest , and StarPlus were searched in December 2023. A supplementary search was conducted in Google Scholar and hand-searching of two key journals within the topic area. The search focused on five geographical locations that share some cultural background (United Kingdom/Ireland, United States, Canada, New Zealand, and Australia). A total of 60 studies and reviews were included which meet the inclusion criteria. The main findings were: (a) diverse populations and marginalized groups are not represented in the current literature; (b) there is a vast number of interchangeable terminologies used; (c) there are limited studies that examine the predictors of victimization of IBSA; (d) the United States and Australia are the dominant countries of study of IBSA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".