SoK: The Dual Nature of Technology in Sexual Abuse
Bibliographic record
Abstract
This paper systematizes and contextualizes the existing body of knowledge on on technology’s dual nature regarding sexual abuse: facilitator of it and assistant to its prevention, reporting, and restriction. By reviewing 224 research papers, we identified 10 characteristics of technology that facilitate sexual abuse: covertness, publicness, anonymity, evolution, boundlessness, reproducibility, accessibility, indispensability, malleability, and opaqueness. We also analyzed how technology assists victims and other stakeholders in coping with and responding to sexual abuse. Our research questions examined the challenges in using technology to address sexual abuse too. For instance, its use by victims can lead to revictimization. To address technology’s challenges, we offer recommendations and suggest new research directions. These findings about the dual nature of technology can inform research and development toward better support for victims of sexual abuse.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| 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".