Sexting Legislation In The United States And Abroad: A Call For Uniformity
Bibliographic record
Abstract
In this study, we analyzed the sexting laws of 50 states in the United States (U.S.) and the District of Columbia, as well as five English-speaking international countries (Australia, Canada, England, New Zealand, and South Africa). We also examined laws related to aggravated circumstances, such as in cases of revenge porn. Our analyses revealed considerable variation, both in U.S. and international law, with some jurisdictions relying on archaic child pornography statutes to prosecute teenage sexting cases and others, developing new, extensive legislation that addresses various types of online interactions (e.g., sexting, revenge porn, and cyber bullying). Additionally, in jurisdictions where specific teenage sexting legislation has not been adopted, there is often a disconnect between these child pornography statutes, laws related to age of sexual consent, and typical teenage sexting behavior. This incongruity creates an abstruse landscape for teenagers to determine the legality of their sexting behaviors. Using the psychological research on the topic of sexting as a basis for our discussion, we highlight the state-level and national legislation that attempts to address these issues comprehensively. Moreover, we make legislative recommendations and advocate for more uniformity across jurisdictions and lesser penalties in teenage sexting cases.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".