Dream It, Do It? Associations between Pornography Use, Risky Sexual Behaviour, Sexual Preoccupation and Sexting Behaviours among Young Australian Adults
Bibliographic record
Abstract
While sexting behaviours have attracted increasing research focus over the last decade as both normative and deviant forms of sexual activity, little attention has been paid to their potential associations with sexual preoccupation and heightened interest in sex. The current study sought to identify whether sexual preoccupation significantly predicts sending, receiving, and disseminating sexts, after controlling for pornography use and risky sexual behaviours. Young Australian adult participants (N = 654, 78.8% women) aged 18 to 34 (M = 19.78, SD = 1.66) completed an anonymous online self-report questionnaire regarding their engagement in sexting behaviours (sending, receiving, and dissemination), pornography use, risky sexual behaviours, and sexual preoccupation. Results showed that individuals with higher sexual preoccupation were more likely to engage in pornography use and risky sexual behaviours. Binary hierarchical logistic regressions revealed that sexual preoccupation predicted higher rates of sending and receiving sexts. However, sexual preoccupation did not significantly contribute to increased rates of sext dissemination. Our study illustrates the need to incorporate pornography viewing and sexting into the promotion of safe sexual behaviours in online and offline contexts, and the potential to utilise modern technology to negotiate safer sex practices.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".