Sexting as media production: Rethinking social media and sexuality
Bibliographic record
Abstract
Many of the responses to teen sexting are ineffective and unjust: authorities sometimes blame the victims of nonconsensual sexting, use harsh child pornography laws against minors, and give teenagers the advice to simply abstain. While some scholars champion girls’ media production practices, mass media coverage of girls’ social media use since the early 2000s emphasizes concerns that girls are creating and sharing sexual content. In this paper, I illustrate and challenge common concerns about the negative effect of digital and mobile media on how girls communicate and who they can communicate with. I argue that thinking about sexting as media production would encourage researchers to pay more attention to the opportunities of social media as well as the risks. Thinking about consensual sexting as an act of media authorship also pushes models of media production to better account for the privacy rights of people who create social media content.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.065 |
| Scholarly communication | 0.018 | 0.030 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| 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".