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Record W2565852789 · doi:10.5296/jsss.v4i1.10279

Types of Pleasures Occurring through Paid Sexual Performances among Women Offering Escort Services

2016· article· en· W2565852789 on OpenAlexafffund
Jacqueline Comte

Bibliographic record

VenueJournal of Social Science Studies · 2016
Typearticle
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsUniversité Laval
FundersUniversité Laval
KeywordsPleasureFeelingHuman sexualityShamePsychologyEmpathySocial psychologyDisgustCompetence (human resources)Gender studiesSociologyPsychotherapist

Abstract

fetched live from OpenAlex

Even though offering sexual services implies for service providers that they have to deal directly with sexuality, research rarely focuses on their representations and emotional experience of sexuality. In order to explore both of these aspects, 16 women who either were offering or had offered escort services were individually met for two semi-directed interviews of about 90 minutes each. Most participants either pursued or accepted sexual pleasure in their interaction with clients, while a few others rather avoided it. In this article, I will first review the skills participants considered necessary for a good work performance (self-presentation as beautiful, feminine and sexy; listening skills and empathy; sexual competence). Following this, I will present the experiences of different types of pleasure they identified (regarding work well done; resulting from performing sexually; feeling sexual pleasure) and compare these experiences as well as the representations those pursuing or accepting sexual pleasure had in contrast with those avoiding sexual pleasure. Feeling comfortable in having sex outside a love relationship as well as in being paid for it renders these types of pleasure possible, while considering sexuality should be expressed only with lovers produces shame, disgust and much displeasure when doing the paid sexual performance.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.727

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.062
GPT teacher head0.392
Teacher spread0.330 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2016
Admission routes2
Has abstractyes

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