Erobots as research tools: Overcoming the ethical and methodological challenges of sexology
Bibliographic record
Abstract
Sexology faces several ethical and methodological challenges. One of them is that sex researchers must rely on proxy methods to safely study fundamental aspects of human sexuality – in laboratories and natural environments. However, laboratory studies often lack ecological validity, whereas studies conducted in natural environments make it difficult for researchers to control experimental conditions or use sophisticated equipment. Together, this puts into question some of the empirical foundations of contemporary sexology. To address this problem, the present article proposes that sex researchers could leverage the potential of emerging technology, like erobots – or artificial erotic agents, such virtual partners, erotic chatbots, and sex robots – to help overcome some of the current ethical and methodological challenges of sexology. To make this case, this article describes these challenges; highlights how erobotic technologies could be employed as research tools to conduct more ecologically valid sexological studies safely and ethically in and outside laboratory settings; and discusses the relative strengths and weaknesses of embodied, virtual, and augmented erobots as experimental apparatus in sex research. Ultimately, this article concludes that the development of erobots that are useful for sexology may require further collaboration between academia and the private sector. It also concludes that the development of such useful erobots may allow us to gain a deeper understanding of ourselves and our eroticism.
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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 | 0.162 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.069 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".