Bibliographic record
Abstract
Sexuality, as it relates to video games in particular, has received increasing attention over the past decade in studies of games and play, even as the notion of play remains relatively underexplored within sexuality studies. This special issue asks what shift is effected when sexual representation, networked forms of connecting and relating, and the experimentation with sexual likes are approached through the notion of play. Bringing together the notions of sex and play, it both foregrounds the role of experimentation and improvisation in sexual pleasure practices and inquires after the rules and norms that these are embedded in. Contributors to this special issue combine the study of sexuality with diverse theoretical conceptions of play in order to explore the entanglements of affect, cognition, and the somatic in sexual lives, broadening current understandings of how these are lived through repetitive routines and improvisational sprees alike. In so doing, they focus on the specific sites and scenes where sexual play unfolds (from constantly morphing online pornographic archives to on- and offline party spaces, dungeons, and saunas), while also attending to the props and objects of play (from sex toys and orgasmic vocalizations to sensation-enhancing chemicals and pornographic imageries), as well as the social and technological settings where these activities occur. This introduction offers a brief overview of the rationale of thinking sex in and as play, before presenting the articles that make up this special issue.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.401 | 0.231 |
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".