Sending, receiving, and nonconsensually sharing nude or near‐nude images by youth
Bibliographic record
Abstract
INTRODUCTION: There is growing evidence about the prevalence of sending, receiving, or resharing nude images by youth (sexting). Less is known about the demographic, technology use, and social context correlates of sexting. Using logistic regression, we looked at predictors of sexting behaviors in minors. METHODS: = 13.5, SD = 2.50, 60.3% females) recruited for an anonymous online survey in the United States. The survey comprised questions about demographic characteristics, sexting behaviors, technology use, attitudes, and perceived norms. The four outcomes were sending nude or near-nude images or videos (images), receiving images sent without the depicted person's knowledge, nonconsensually resharing images, or having one's own images nonconsensually reshared. RESULTS: Regression analyses showed gender, gender/sexual minority status, use of dating apps and particular online platforms, self-sharing and resharing attitudes, and friend norms predicted sending images. Age, resharing attitudes, and friend norms all predicted receiving nude images of other youths. Household income, geographic location, some online platforms, resharing attitudes, and friend norms all predicted nonconsensual resharing of nudes. Age, use of encrypted apps, and friend norms predicted having one's own image nonconsensually reshared. CONCLUSIONS: We partially replicated prior research by finding associations between age and gender. Further, we identified technology use factors including the use of dating apps and particular platforms. Attitudes about sexting and perceived friend norms were robust across sexting behaviors, suggesting these factors are potentially important for intervention.
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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 | 0.002 | 0.001 |
| 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".