Sexcams in a Dollhouse: \nSocial Reproduction and the Platform Economy
Bibliographic record
Abstract
Once a peripheral phenomenon on the Web, sexcam platforms have been gaining social and economic importance, attracting millions of visitors every day. Crucial to this popularity is the technical and economic model that some of those sites use. Sexcam platforms combine the practices of labor and user-generated platforms. As platforms, they mediate between users and providers, becoming the field where those operations occur. Sexcam platforms, however, are more than intermediaries, and their structures incorporate and reproduce discriminatory conventions. \n \nSexcams in a Dollhouse: Social Reproduction and the Platform Economy is a research-creation project exploring digital labor through the American sexcam platform Chaturbate.com. Rather than treating this platform as an exception, this project invites the consideration of Chaturbate as a paradigmatic instance of work in the context of platform capitalism. Sexcam platforms, this research argues, illustrate recent changes in the notions of what is work and what is leisure, what generates value, or the shifting nature of social relations through social media. \n \nUsing a made-up dollhouse as an interface and stage, this project set up a series of performative interventions on the sexcam platform. Through humorous yet critical play, these pieces asked about the situation of social reproduction on the platform economy, the role of maintenance practices in the generation of value, and the incorporation of new technological infrastructures into daily life.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".