Four Problems in Sexting Research and Their Solutions
Bibliographic record
Abstract
Despite over 10 years of research, we still know very little about people’s sexting behaviours and experiences. Our limited and, at times, conflicting knowledge about sexting is due to re-searchers’ use of inconsistent conceptual definitions of sexting, dubious measurement practices, and atheoretical research designs. In this article, we provide an overview of the history of sex-ting research and describe how researchers have contributed to the ‘moral panic’ narrative that continues to surround popular media discourse about sexting. We identify four key problems that still plague sexting research today: (1) imprudent focus on the medium, (2) inconsistent conceptual definitions, (3) poor measurement practices, and (4) a lack of theoretical frameworks. We describe and expand on solutions to address each of these problems. In particular, we focus on the need to shift empirical attention away from sexting and towards the behavioural domain of technology-mediated sexual interaction. We believe that the implementation of these solu-tions will lead to valid and sustainable knowledge development on technology-mediated sexual interactions, including sexting.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| 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".