“Damned if you do, damned if you don’t”: Women’s accounts of feigning sexual pleasure
Bibliographic record
Abstract
Faking orgasm has been identified as a common practice among women and feminist scholars have probed the connections between the socio-cultural meanings associated with faking and heterosex. Expanding on this line of inquiry, feigning sexual pleasure was explored in interviews with 14 women who reported having sex with men. Using a feminist critical discourse analytic approach, we attend to the dilemma that was frequently evoked in women’s accounts. Participants explained that feigning sexual pleasure was done in order to protect their partners’ ego. However, participants also talked about faking orgasm as being problematic in the sense that it was “deceitful” and “dishonest”. These contrasting discursive patterns created a dilemma whereby faking was situated as “necessary” but “dishonest”. As a way of negotiating this dilemma, participants made a distinction between exaggerating sexual pleasure and faking orgasm. We posit that exaggeration can be interpreted as a form of material (during the sexual encounter) and discursive (during accounting of the encounter) disruption of dominant discourses of heterosex such as the orgasmic imperative. Drawing on Annamarie Jagose’s and Hannah Frith’s problematizations of the prevailing tendency to position orgasm as either “authentic” or “fake”, we discuss women’s negotiation of the limited constructions of “real” pleasure.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.033 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".