Non-consensual forwarding of sexts: characteristics and overlap with in-person sexual coercion
Bibliographic record
Abstract
This study sought to describe the characteristics of people who non-consensually forward sexts, to examine the overlap between the non-consensual forwarding of sexts and in-person sexual coercion, and to investigate what correlates were associated with each perpetration type (i.e. the non-consensual forwarding of sexts and in-person sexual coercion). In our online community sample of 2,780 emerging adults (i.e. aged 18–30), mostly from North America (97.9%), we found a prevalence of 9.2% for the non-consensual forwarding of sexts and a 13.7% prevalence for in-person sexual coercion. The two types of sexual coercion overlapped; however, more perpetrators of the non-consensual forwarding of sexts had also committed in-person perpetration than in-person perpetrators who also committed the non-consensual forwarding of sexts. Higher sex drive, being a man, greater susceptibility to peer pressure, and self-reporting the other type of sexual coercion were independently related with in-person sexual coercion and the non-consensual forwarding of sexts. Our findings suggest a possible overlapping etiology between in-person sexual coercion and the non-consensual forwarding of sexts and that programmes aimed at reducing in-person sexual coercion could be effective for reducing the non-consensual forwarding of sexts.PRACTICE IMPACT STATEMENT Results from our online survey of 2,780 adults aged 18-30 suggest that using “revenge pornography” to refer to the non-consensual forwarding of sexual materials is unnecessarily restrictive and does not represent the nature of this phenomenon. Further, we found that positive beliefs about the non-consensual forwarding of sexts was a common motivation for non-consensually forwarding sexts, suggesting that social media campaigns educating emerging adults on the risks of the non-consensual forwarding of sexts may be effective in reducing this behaviour.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".