Investigating differences between sexters and non-sexters on attitudes, subjective norms, and risky sexual behaviours
Bibliographic record
Abstract
Computer-mediated communication (CMC) is often used to initiate, maintain, or terminate intimate relationships and recently, such platforms have been considered an outlet for sexual communication. This has led to the emerging trend of sending sexually suggestive messages via computer devices in what is known as “sexting.” The current study expands the definition of sexting to include different types of sext content (i.e., non-sexters, less explicit, explicit, and very explicit) and modes of transmission (e.g., cell phone, social networking). Our primary goal was to determine whether sexting behaviours, risky health behaviours, attitudes and subjective norms, sensation seeking, and motivations for sexting differ across separate sexter groups (N=511). Individuals who had never sent a sext message were classified as non-sexters (n=117), those who had sent sexy word-based messages were classified as less explicit sexters (n=135), semi-nude photo or video senders were classified as explicit sexters (n=87), and individuals who had sent fully nude photos or videos were classified as very explicit sexters (n=172). Results revealed that participants who report very explicit forms of sexting had higher positive attitudes toward sexting and engaged in riskier sexual behaviours relative to explicit, less explicit and non-sexters. In general, sexters perceived more social pressure to engage in sexting and demonstrated a higher need for sensation seeking compared to non-sexters. Higher rates of alcohol consumption were found among the very explicit and explicit sexter groups compared to less explicit and non-sexters.
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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 | 0.003 | 0.000 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".