Technically in love: individual differences in desire for intimacy with robots
Bibliographic record
Abstract
Engineers have begun creating robots that look and act human, with the aim of maximizing the likability of real-life robot partners for friendship and sex. In science fiction, robots often look and act human, and these robot characters usually develop interpersonal relationships with human characters. Researchers have begun creating robots like those depicted in science fiction, gauging the beliefs of participants to maximize the likability of robot partners in real life. This thesis explored how today’s Canadian undergraduates view robots, and if they would want to have a robot as a friend, or to have sex with a robot. I measured participant Robosexuality, or participant interest in having sex with a robot, and Robofriendship, or participant interest in having a robot friend. I also measured how sociosexual orientation, social dominance orientation, hostile sexism, and gender relate to Robosexuality and Robofriendship, including a mediation that examined if men are more sexist than women, and if this sexism explain men’s higher Robosexuality. Participants varied widely in their expressed interest in close relationships with robots, with almost flat distributions across both scales. Sociosexual orientation, social dominance orientation, gender, and hostile sexism all predicted Robosexuality, but only hostile sexism predicted Robofriendship. Results from the mediation showed that hostile sexism partially explained the relation between gender and Robosexuality. I conclude by discussing the limitations and future directions for this research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".