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Record W4229024990 · doi:10.33137/ijournal.v7i2.38615

Platform Pimps

2022· article· en· W4229024990 on OpenAlexaffvenue
Aria Slippert

Bibliographic record

VenueThe iJournal Student Journal of the Faculty of Information · 2022
Typearticle
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPanopticonGuard (computer science)PrisonSociologyGazeMedia studiesSubject (documents)AffordanceComputer securityInternet privacyComputer scienceWorld Wide WebLawCriminologyPolitical sciencePoliticsHuman–computer interactionArtificial intelligence

Abstract

fetched live from OpenAlex

This essay compares the complex hierarchy of surveillance embedded in the content sharing platform OnlyFans to Jeremy Bentham’s panopticon prison. OnlyFans creators have found a sweet spot in a novel surveillant assemblage, capitalizing on the voyeuristic tendencies of their followers who are subsequently subject to data surveillance by the platform and its intermediaries. In the classic panopticon, prisoners are held captive in a circular room under the gaze of a single guard in a tower, unable to determine if and when they are being watched. In the inverted OnlyFans panopticon, the content creator stands alone in the tower and captivates their invisible audience below. Each creator maintains a single porn-opticon (pornographic-opticon) to be ultimately surveilled by the platform, a faceless, bodiless puppet master running the show from behind the scenes. Through this analogy, this essay explores the particular set of affordances that set OnlyFans and its creators apart from other platforms and forms of sex work.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.325
Threshold uncertainty score0.963

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0040.004
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.3250.113

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 head0.046
GPT teacher head0.363
Teacher spread0.317 · 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.

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
Published2022
Admission routes2
Has abstractyes

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Same venueThe iJournal Student Journal of the Faculty of InformationSame topicSexuality, Behavior, and TechnologyFrench-language works237,207