Sexting Victimization Among Dating App Users: A Comparison of U.S. and Chinese College Students
Bibliographic record
Abstract
The widespread use of digital technology and devices has fundamentally transformed people's social life in recent decades, particularly in interpersonal relationships. Two popular social phenomena elucidate how social connections and interactions have dramatically evolved due to technological advancement. Sexting has surfaced as a popular way of getting attention or flirting among young populations over the past decade. Online dating also has emerged as a viable avenue for people to seek interpersonal romantic and/or sexual relationships. Based on survey data collected from two Chinese universities and one U.S. university, this study links sexting and online dating by comparatively assessing the prevalence of sexting victimization and factors influencing such victimization among young online daters. Bivariate and multiple analyses reveal that American college students are more inclined than their Chinese counterparts to be victims of receiving sexts. Chinese students with higher degrees of rape myth acceptance are more likely to experience sexting victimization, but such an association does not exist among U.S. students. Internet-related activities were only weakly connected to sexting victimization among college students. LGBT young adults, regardless of their country affiliation, are at a higher risk for sexting misconduct. Female and younger American students were more likely to experience sexting victimization, whereas Chinese students in a romantic relationship were more inclined to experience sexting victimization. If possible, future research should employ a random sampling strategy to draw a larger number of college students from different types of universities in different regions. Future studies should include other theoretically relevant variables, such as self-control and opportunity variables, into the sexting victimization research.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".