Perceived stigma and erotic technology: From sex toys to erobots
Bibliographic record
Abstract
The intersection of technology and sexuality in sex toys and erobots – artificial erotic agents (e.g. sex robots) – may generate stigma with their use. However, despite the growing prevalence of technology in human sexuality, researchers have yet to examine this stigma. Hence, this study provides the first quantitative evidence of perceived stigma related to erotic technology use (PSETU) and its association with people’s willingness to engage with erotic technologies. Based on previous research, we hypothesised that PSETU exists and increases as a function of products’ human-likeness (Hypothesis 1), and negatively correlates to participants’ willingness to engage with erotic technologies (Hypothesis 2), with stronger associations for women and sex toys and stronger associations for men and erobots (Hypothesis 3). A convenience sample of 365 adults (≥18 years; with access to the recruitment material) completed an online survey measuring their PSETU for sex toys, erotic chatbots, virtual partners, and sex robots, and their willingness to engage with these technologies. The results support Hypothesis 1, and partly support Hypotheses 2–3. Women and men also perceive the same technology-related stigma. These findings are important given the prevalence of sex toys, the advent of erobots, and the potential impact of stigma on their (future) users.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".