Technically in Love: Individual Differences relating to Sexual and Platonic Relationships with Robots
Bibliographic record
Abstract
Incremental advancements in technology present researchers with unique opportunities to examine and predict human behavior not only during, but before the integration of technology into daily life. While human-like, autonomous robots are still a thing of science fiction, digital assistant technology is becoming increasingly prevalent in various industries, like entertainment and business. Previous studies have identified trends in both the design and reception of these 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 determine whether preconceived beliefs about gender inequality and social hierarchies would predict individuals’ interest in engaging in platonic friendships (“robofriendship”) or sexual relationships (“robosexuality”) with hypothetical human-like robots. Gender, ambivalent sexism, social dominance orientation, and sociosexual orientation were used to predict 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.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".