Bibliographic record
Abstract
This thesis explores sexual labour on the virtual platform OnlyFans to understand the labour process of sex workers within a gig work platform. This research utilizes interpretive qualitative methods such as interviews and virtual participant watching to understand the experiences of four sex workers who utilize the platform. Employing McDonaldization as a conceptual framework, my thesis argues that labour on OnlyFans is a unique intersection between gig work and voluntary sex work. This thesis addresses the following questions: 1) How has the intersection between gig work and sex work on virtual platforms attracted people to sex work as a form of labour? 2) How does this impact our way of understanding, and regulation of, online sex work? 3) How do sex workers on a gig work platform define the work they do? Three major themes emerged from the data, including an understanding of the labour process on the platform, using the voice of my participants to define the work they are engaging in, and issues experienced with platform-based work. The findings demonstrate that McDonaldized aspects of the platform, such as efficiency with distributing content and predictability through similarities with social media sites, have drawn sex workers and non-sex workers to use OnlyFans as a platform. Further, given the changing nature of societal attitudes and views towards sexual content, participants indicated that they are simply capitalizing on content they would have shared on alternate social media sites. Finally, findings suggest that many aspects of the labour are comparable to that of gig workers on platforms such as Uber, Etsy, or SkipTheDishes. I suggest that certain forms of platform-based sex work require a regulatory framework which differs from current Canadian sex work regulations which criminalize the purchase of sexual labour, and instead, one which recognizes the voluntary and gig-work aspect of the labour.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".