Gross Double Standard! Men Using Sextech Elicit Stronger Disgust Ratings Than Do Women
Bibliographic record
Abstract
Use of vibrators and more advanced forms of sextech is increasingly common, yet remains stigmatized. Disgust, an emotion linked to social attitudes and sexual norm violations, may contribute to this stigma. However, research has yet to examine perceptions of sextech use as disgusting, or how these perceptions vary by gender. To address this, we tested whether: H1) men’s sextech use is rated as more disgusting than women’s; H2) disgust increases with the humanlikeness of the device; and H3) women report greater disgust than men across scenarios. Results from a survey (n = 371) revealed that men were viewed as more disgusting than were women when depicted as using sextech. Additionally, disgust levels varied depending on the device depicted, with sex toy use eliciting the least disgust and sex robot use the most. Across all items, women participants reported higher disgust than did men. These findings provide the first evidence of a sexual double standard penalizing men for sextech use, and that sextech use is viewed as more disgusting as it becomes more humanlike. These findings advance work promoting integration of technology and acceptance and normalization of varied sexual behaviors as they become increasingly incorporated into people’s sex lives.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".