MétaCan
Menu
Back to cohort

OnlyFans: The Celebritization of Online Sexual Labour

2023· article· en· W4387434291 on OpenAlexaffvenue
Mary McCluskey

Bibliographic record

VenueCanadian Graduate Journal of Sociology and Criminology · 2023
Typearticle
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsCarleton University
Fundersnot available
KeywordsPopularityPornographySex workSex workersWork (physics)Coronavirus disease 2019 (COVID-19)PandemicAdvertisingSociologyPsychologyBusinessSocial psychologyEngineeringPopulationDemographyHuman immunodeficiency virus (HIV)MedicineResearch methodology

Abstract

fetched live from OpenAlex

Throughout the COVID-19 pandemic, there has been a shift towards digital forms of employment, including sex work. The creation of the online platform, OnlyFans, has allowed sex workers to sell sexual content on a site that is easily accessible and readily available. However, the rising popularity of the platform drew attention to celebrity presence and removed the ability for sex workers to be successful, as the presence of celebrities made changes to the way individuals could advertise and benefit financially. Although previous research addresses the nature of online pornography and sex work, there is a dearth of knowledge regarding the site “OnlyFans” and its impact on the current age of online sexual labour. Therefore, this research aims to highlight the celebritization of the platform “Only Fans” and its impact on online sexual labour.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0070.006
Scholarly communication0.0070.006
Open science0.0010.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0150.001

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.

Opus teacher head0.158
GPT teacher head0.359
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Graduate Journal of Sociology and CriminologySame topicSexuality, Behavior, and TechnologyFrench-language works237,207