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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".