Bibliographic record
Abstract
Recently, national and local media across North America have warned against the risks of ‘sexting’ – the practice of sending, posting or possessing sexually suggestive text messages and images via cell phones or the Internet. In response to this phenomenon, Pennsylvania District Attorney Skumanick threatened to bring child pornography charges against teenagers who had been caught sexting and who refused to attend a gender-based ‘re-education’ program designed to teach them about its dangers. Three girls refused the ultimatum, resulting in Miller v. Mitchell [2010], the first case to challenge the constitutionality of prosecuting teens for their digital sexual expression. This article critically considers dominant and intersecting cultural and legal narratives about sexting and troubles the predominant construction of teenage female sexters as dupes of the ‘pornification’ of a generation and as ‘self-sexually exploiting.’ The cultural and legal disavowal of girls’ narratives about digital sexual expression is considered through Judith Butler’s poststructural analysis of sexuality, speech and censorship. Drawing on two online studies of sexting, contributions to an online forum on the topic, and third-wave feminist writings on a generational re-envisioning of risk, respectability and privacy, I argue that that the foreclosure of the ‘domain of the sayable’ within which girls seek to speak works paradoxically to further render them fetishized sexual objects, thus engendering the very harm that criminal law seeks to remedy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".