How Young Canadian Women Make Sense of their Experiences of Sexting
Bibliographic record
Abstract
Sexting, which refers to the exchange of erotic content via mobile or internet device, is an increasingly common activity among emerging adults (Lenhart, Purcell, Smith, & Zickuhr, 2010; PEW Research Centre, 2010; Samimi & Alderson, 2014). Much of the current research on sexting focuses on the associations between sexting and risk or harm, specifically for women (e.g. Benotsch, Snipes, Martin, & Bull, 2013; Dir, Cyders, & Coskunpinar, 2013; Perkins, Becker, Tehee, & Mackelprang, 2014). However, emerging research suggests sexting can also have benefits for young women, including experiences of pleasure and opportunities for self-exploration (Bond, 2011; Ringrose, Gill, & Livingstone, 2013). The purpose of this research was to investigate how young Canadian women describe their experiences with sexting. Specifically the study answered the following research questions: (1) How/in what ways do young Canadian women use sexting? (2) What role does sexting play in young Canadian women’s lives, sexualities, and relationships? (3) How does sexting impact young Canadian women? (4) What do young Canadian women need to support safe and enjoyable sexting? Thirty women between the ages of 18-25 were recruited online from Southern Ontario colleges and universities and were interviewed using semi-structured interviews. Thematic analysis suggested 7 key themes (in no specific order). Sexting supports: (1) Empowerment: from pleasing others to speaking up more and attending to their own sexual needs; (2) Pleasure: from the expansion of sexual possibilities; (3) Safety and Comfort: sexting was described as safer and more comfortable than in-person communications, but led to increased fears about privacy; (4) Self-Discovery: by aiding in the exploration of sexual tastes and orientation; (5) Optimal Self-Representation: helping women “craft” ideal representations of themselves; and (6) Intimacy and Connection: helping to increase trust, improve communication, and maintain relationships. The women also described the impact of, and how they navigated or resisted, gendered sexual expectations ((7) Sexual Scripts). The study contributes to the literature by helping contextualise young women’s experiences of sexting and illuminating how young women could be better supported in their sexting behaviours. The results of this study can be used inform policy, curriculum and advocacy initiatives.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".