Bibliographic record
Abstract
Despite its ability to arouse and titillate or disgust and anger (and sometimes both simultaneously), porn has only recently been examined through an affective lens. Writing in the inaugural issue of Porn Studies, Susanna Paasonen (2014) advocates for porn studies scholars to consider how the application of affect theory can help us better understand the appeal of pornography. Drawing on Paasonen’s concept carnal resonance and Margaret Wetherell’s (2012) affective practice, I propose the concept _carnal practice_ as a way to examine how one engages and makes sense of online pornography through the affectively felt practice of searching for, finding, and getting off to online pornography. This is done through an analysis of 48 semi-structured interviews, conducted between February 2019 and September 2020 and in 5 countries—Taiwan, South Korea, China, Japan and Canada—in which I discuss how queer East Asian men navigate their respective internet space, are drawn to particular content and platforms that satisfy their desires and curiosities, and make sense of the porn they view. In some instances, participants discussed notions of being drawn to authenticity in porn as well as porn that seems real (that is, most similar to their lived experience), attraction to particular sex acts, scenarios and races, and, in other cases, allowing a platform’s algorithm to help facilitate and fine-tune their desires, to name a few. Although this study focuses specifically on the practices of queer East Asian men, the concept of carnal practice is not limited to this group and has wider application.
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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 | 0.001 | 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 | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".