“If a girl’s photo gets sent around, that’s a way bigger deal than if a guy’s photo gets sent around”: Gender, sexting, and the teenage years
Bibliographic record
Abstract
Youth, particularly female teens, are encouraged to self-monitor and be responsible for their actions online in order to avoid harm from cyberbullying, ‘sexting,’ and other forms of cyber-risk. Highlighting findings from 35 focus groups with Canadian teens regarding sexting, we show the continued saliency of a gendered double-standard applied to the online distribution of nudes. Our sample of male and female teens (n=115) from urban and rural regions, aged 13-19, underscores the relatively lower ‘stakes’ involved with sexting for male teens. We explore this trend with specific reference to the salience of hegemonic masculinities and the gendered aspects of public and private spaces, both online and offline. Public exposure of nudes has potentially serious stigmatizing consequences for youth. We highlight teen experiences sending and receiving images of male penises (‘dick pics’), which is an under-researched aspect of sexting. We show the relative ubiquity of receiving ‘dick pics’ among female teens, exploring various reactions, and male motivations for doing so from male and female standpoints. Policy implications are discussed with specific reference to school-based cyber-safety programs, which our participants indicate remain highly-gendered, neglecting epistemological questions around male experiences and responsibility.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".