‘Who can handle a true BBC?’: masculinities, race and dick pic sharing on Reddit
Bibliographic record
Abstract
This paper critically examines the intersections of masculinity and race in relation to pornographic self-representation (PSR) on Reddit with a primary focus on r/MassiveCock. We argue that Massive is a site of dick pic curation that is structured by unmarked Whiteness, although its norms are disturbed by the presence of a minority of non-White racialized posters. Drawing on Cruz's (2016) notion of a politics of perversion and Miller-Young's (2008; 2010) concept of counter-fetishization, we discuss the porn performances of White, Black and MOC (men of colour) posters who mark out a racialized identity through textual cues such as usernames and post titles. We argue that while the White posters appropriate the BBC, incorporating it into hegemonic masculinity, Black posters directly mobilize it and MOC posters poach the trope to position themselves as active desiring subjects rather than passive objects/abjections of the White pornographic imaginary. Finally, we present data on visits of the two latter groups to a cluster of subreddits dedicated to non-White dick pic curation. We illustrate how some of these Redditors range through these porn geographies, potentiating perverse pleasures in the process.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".