Technically in love: Individual differences relating to sexual and platonic relationships with robots
Bibliographic record
Abstract
Incremental advancements in technology present researchers with opportunities to examine and predict human behavior before the integration of technology into daily life. Previous studies have identified trends in both the design and reception of current social robotic technologies, including gender biases and social “othering”, which may affect how humans interact with more advanced robotic technologies in the future. The aim of the current study was to explore whether preconceived beliefs about gender inequality, interest in casual sex, and social hierarchies would relate individuals’ interest in engaging in platonic friendships (“robofriendship”) or sexual relationships (“robosexuality”) with hypothetical human-like robots. Two-hundred and twelve participants completed an online survey measuring gender, ambivalent sexism, social dominance orientation, and sociosexual orientation in relation to individuals’ interest in both robofriendship and robosexuality. It was found that hostile sexism positively predicted interest in robosexuality, particularly for men (β = .16, b = .27, 95% CI [.03, .30], t(209) = 2.364, p = .019). Conversely, hostile sexism negatively predicted robofriendship, and significant interactions effects were found in that at lower levels of SDO, women maintained greater interest in robofriendship than men (β = .26, b = .54, 95% CI [.09, .99], t(208) = −2.235, p = .02). The current study provides preliminary evidence to suggest that preconceived beliefs about social hierarchy and gender inequality may impact romantic and platonic interactions between humans and robots. Limitations and future directions are also discussed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".