“Don’t fake the big O”: Portrayals of faking orgasm among women in<i>Cosmo</i>and<i>Glamour</i>
Bibliographic record
Abstract
Many women report faking orgasm, at least on occasion, during heterosex (i.e., heterosexual sexual activities). The reasons for the practice include validating the skill of a male lover, a way of ending sexual encounters, and to avoid pathologization that is often associated with orgasmic absence. Constructions of heterosex, female sexuality, and sexual pleasure are influenced by multiple sources including the media. However, there is a lack of systematic research on how faking orgasm is presented in the media. This gap in research provided an opportunity to investigate women’s magazines’ portrayals of the practice. Using constructionist thematic analysis, 69 online articles, published by two popular women’s magazines, Cosmopolitan and Glamour, were analyzed. From the examined articles, two major themes emerged: faking as a common practice (especially via women’s first-hand accounts) and instructing the reader not to fake. It is our contention that together the two themes create a distinctly postfeminist portrayal of faking orgasm. On the one hand, the magazines included and highlighted women’s own experiences and reasons for faking orgasm. On the other hand, female readers were instructed to avoid faking in the name of personal responsibility and empowerment. Notably, the magazines omitted any discussion of gender power relations in their appeals for choice and sexual agency. Overall, this study expands our understanding of women magazines’ contradictory portrayals of women’s sexuality, sexual pleasure, and navigation of its absence.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".