Bibliographic record
Abstract
This creative response interrogates the persona that seems to inhabit sex webcam platforms, online services where people can stream and monetize amateur sexual performances (Henry & Farvid 2017; Jones 2020). If the perception of authenticity has been a crucial feature of camming since its inception (Senft 2008), this impression is no longer conveyed only through amateur signifiers (Hernández 2019). Domesticity on the sexcam platform oscillates between two poles. One of them is incarnated by the professional webcam studio, where uninhabited rooms are presented as private yet generic spaces (Korody 2019). The other pole is the personal space, staged for its transmission through the platform. Decorative trends and habits crossover between these extremes, creating a new type of domesticity with no other purpose than the sexual spectacle. The product of this mutual influence is referred to as ‘post-authentic domesticity’ in this article. Drawing upon literary studies, post-authenticity implies a fiction that engages with an ‘authentic’ referent but does not aim to replicate it (Gefter Wondrich 2020). As such, domesticity in the sexcam platform is both a second-hand reference and a space where its online persona unfolds.The exploration of this post-authentic domesticity is conveyed by means of a website that replicates some of the graphic conventions of sexcam performers’ profiles. The website is divided into different pages that address distinct aspects in the form of short poems. The images accompanying the texts, created by the author, were made using ASCII characters and they reference empty webcam rooms observed in the sexcam platform Chaturbate during 2022. The abstract character of the images aims to emphasize the role of the audience in the construction of online intimacy and meaning.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".