Consensual Sexting among College Students: The Interplay of Coercion and Intimate Partner Aggression in Perceived Consequences of Sexting
Bibliographic record
Abstract
Recent empirical data suggests that the majority of adolescents and emerging adults utilize digital technology to engage with texting and social media on a daily basis, with many using these mediums to engage in sexting (sending sexual texts, pictures, or videos via digital mediums). While research in the last decade has disproportionately focused on the potential risk factors and negative consequences associated with sexting, the data are limited by failing to differentiate consensual from non-consensual sexting and account for potential influences of intimate partner aggression (IPA) and sexting coercion in these contexts. In the current study, we assessed the positive and negative consequences associated with sexting, using behavioral theory as a framework, to determine the relationship between an individual’s personal history of IPA victimization and the perceived consequences. Undergraduate students (N = 536) who reported consensual sexting completed a series of measures examining their most recent sexting experience, including perceived sexting consequences, and their history of sexting coercion and IPA. Results suggested that those reporting a history of any type of IPA victimization endorsed more negative reinforcing consequences after sending a sext, and those with a history of physical or sexual IPA victimization endorsed more punishing consequences after sending a sext than those without such history. Additionally, experience with IPA was found to be positively correlated with perceived pressure/coercion to send a sext. The implications of these data for research, policy, prevention, and intervention are explored.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| 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 | 0.002 | 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".