Bibliographic record
Abstract
This is only metadata. The data is not available for reuse without collaboration with the researcher. The dataset consists of 24 interviews with adult webcam models (22 in the United States, 1 in Canada, and 1 in a post-Soviet country). The dataset also includes interviews with 2 clients of adult webcam models, 1 adult entertainment lawyer, 1 worker at a non-profit organization supporting sex workers, 2 website operators, 1 webcam studio operator and performer, and 1 virtual studio operator. There are a total of 32 interviews. The interviews were recorded in person (4) and online via video chat (28). Interviews range in length from 45 minutes to 5 hours, with the majority of the interviews lasting around 2.5 hours. The majority of the interviews have been transcribed and pseudonymized (26). 6 interviews are only audio recordings. Some topics discussed in the interviews include: social media, webcamming, clients, the regulation of the webcam industry, the impact of the law on webcam performers, mental health, sexual pleasure, sexual acts, kink and BDSM, consent, boundaries, intimacy, relationships, entry into sex work, social stigma, and other topics related to being a webcam performer.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.091 | 0.119 |
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".