Bibliographic record
Abstract
Amateur pornography has often been defined negatively in relationship to its professional counterpart; featuring average-looking people, awkward or disengaged performances, and poorly recorded video. This understanding of amateur performance has yet to be reconsidered in light of the mid-2000s industry shift to ‘reality porn,' which boasts professionally-lit scenes shot on clear, high-definition video, and features normatively good-looking performers—performers who are still billed as ‘amateur.' This paper fleshes out current definitions of reality porn, suggesting that in addition to adopting the aesthetics and conventions of reality television, it adopts its promise: that by putting real people in a contrived scenario it can produce a truthful document of a human encounter. Because the believability of this encounter hinges on the understanding that the performers are appearing as themselves, reality porn must establish their ‘real-life’ status via on-camera interviews and behind-the-scenes vignettes.Taking major gay porn studio Sean Cody and one of its star performers Curtis as an example, this paper suggests that the realism of amateur performance is no longer based upon a lack of skill or less-than-ideal looks, but rather constructed within the video itself and across several scenes, creating a serial narrative of sexual exploration and discovery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.031 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".