‘It’s nice to be appreciated’: Understanding heterosexual men’s engagements with sexting and sharing Dick Pics
Bibliographic record
Abstract
This paper explores heterosexual men’s experiences of sexting with a primary focus on how, when and why men send sexually explicit photos to women. Previous research has focused either on gay and bisexual men’s experiences or considered sexting within a broader youth context. This research considers young men and their engagement with sexting practices and its relationship to how they view and understand their bodies as desirable and sexual. Drawing from work that has called for more reflexive considerations of men’s emotions and sexuality, we explore the processes by which men engage in the practice of sexting (how/where they take photos), the affects that sexting provides (how it makes them feel), their rationale for engaging in the practice (why they do it) and their expectations from partners (e.g. reciprocal photos, partner’s responses). The findings of this paper suggest that while men highlight a range of affects and experiences with sexting, on the whole, it helps boost sexual confidence with partners and create and sustain intimacy, particularly in between seeing (in person) a partner or partners. Our research further suggests that men share similar concerns to women in other studies who are concerned about their photos becoming public, thus revealing a primary reason why this particular population of heterosexual men may not engage in the sending of erotic photos.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".