‘I don’t think my torso is anything to write home about’: men’s reflexive production of ‘authentic’ photos for online dating platforms
Bibliographic record
Abstract
This paper explores men’s use of dating apps with an emergent body image focus, addressing cisgender, heterosexual men’s feelings about dating app profile pictures. Drawing from interviews with 15 cisgender, heterosexual men residing in Australia about their use of dating applications including Tinder, Hinge, and Bumble, this paper examines how cisgender, heterosexual men construct their dating app profile pictures, and the decisions they make about the content of images they use for dating profile pictures. Utilizing concepts of self-presentation, authenticity, and bodily reflexive practices, this paper argues that the men in the study are attempting to present authentic and real selves in a dating world, while being confronted by concerns regarding body image and perceptions of ideal bodies. They also demonstrate conflicting desires to appear more muscular, fit, and athletic while not presenting as vain or narcissistic. In the process of creating profiles, these men develop a sense of self drawing on understandings of masculinity and specifically notions of idealized male bodies, which simultaneously run counter to the very authentic images of the self they seek to present.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".