American Parents’ Perceptions of Child Explicit Image Sharing
Bibliographic record
Abstract
Parents and other adult caregivers of biologically or sociolegally related children (hereafter, “parents”) can play an important role in the online behavior of children in their care. In this study, we examined parental correlates of three outcomes— talking to their child about image sharing (66% yes); expecting their child had shared sexually explicit images (39% yes); and preparedness if their child’s sexually explicit images were leaked (38% yes)—in a survey of a nationally representative sample of 402 parents in the United States. Regression analyses revealed that talking to one’s child about sexually explicit image sharing was significantly associated with the parent being a mother, having a child in high school, enforcing a higher number of technology rules, knowing about secondary social media accounts, and expecting that their child’s friends share sexually explicit images of themselves. Expecting their child had sent sexually explicit images was significantly predicted by parents having fewer technology rules in place for their child, more permissive parental attitudes about resharing sexually explicit images, and the expectation that their child’s friends or schoolmates had sent sexually explicit images. Unexpectedly, perceived parental preparedness if their child’s sexually explicit images were leaked was significantly predicted by less—rather than more—parental comfort in talking to children about their child’s online activities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".