Sexting, intimacy and criminal acts: translating teenage sextualities
Bibliographic record
Abstract
This chapter examines the legal regulation of young people under the age of 18 with respect to the practice that has come to be called ‘teenage sexting’. Sexting is a term now used mainly to describe the texting of nude or semi-nude pictures, although it can also be used to denote the sending of erotic texts. Using studies, case material and newspaper reports from the USA (Miller v Pennsylvania, 2010 Opinion; Injunction - Tunkhannock, Pennsylvania, USA), Canada (R v. Walsh 2006, cited in Slane, 2010:54), and the UK (media reports on sexting, 2009-2011, as up to 2012 there had been no prosecutions in England & Wales for sexting). Drawing on actor-network theory (Latour 1987) I argue that where legal governance is deployed, both sexting and teenagers are shaped in particular ways to make them available for a judicial response through processes of ‘translation’ (Brown and Capdevila 1999). These processes of translation are the product of, and also depend on, networks of actors – human and non-human – which produce and assemble the social category of ‘sexting teenagers’ and hence as a particular category of actor for scrutiny, classification, and control in specific contexts. In different contexts, networks and translation processes produce different kinds of ‘sexting teenagers’ – the criminal, the misguided, the immoral, or the romantic. These different kinds of sexting teenagers are produced out of the part the law plays in the regulation of this erotic practice (including when it plays little part at all) but their production is simultaneously also part of the mechanism by which the law is or is not deployed as a source of control. Non-legal networks and actors generate translations of consensual teenage sexting which reproduce current anxieties about risks and the futures of young people and de-centre the law as a means of regulation in favour of moral regulation although legal agents remain present as actors in the network. Here teenagers emerge not as criminals but as innocents whose own naiveté makes them gullible, unruly, vulnerable, and in need of pedagogical rescue.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".