Beyond barks and bodies: Pup play and body image in gay, bi, trans, and queer men through a Deleuzian lens
Bibliographic record
Abstract
Body image is a complex issue that affects individuals from all genders and sexual orientations. Within the queer community pup play is a BDSM-related activity where participants take on the role of a pup/dog, complete with barking, crawling, and other canine behaviors. In this research, we explored how identifying as a pup shapes the body image of gay, bisexual, queer, and trans (GBQT) men using a Deleuzian approach. The process of cellphilming included attendance of three online workshops and the creation of a short cellphilm about their body image. Seventeen participants across five groups completed the workshops and submitted a cellphilm. Data was analyzed through discourse analysis, and while our analysis includes workshop quotes and film excerpts, the true depth of the films, capturing elements like lighting and sound, goes beyond the transcribed words. Three overarching discursive considerations are reported: (i) reinforcing body standards for GBTQ men, (ii) capacity for changing feelings about bodies, and (iii) becoming a pup. Participants suggested that although the pup community is more accepting of diverse bodies, hegemonic body standards for GBTQ men still apply. However, participants also described how identifying as a pup and connecting with others in the pup community was a way to navigate body image tensions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".