Technology Exposure Elicits Increased Acceptance of Autonomous Robots and Avatars
Bibliographic record
Abstract
Science fiction has long promised a future within which robots assist humans in many facets of their daily lives, and robot technology is advancing at a pace which suggests that the necessary technology already exists, or may exist, in the near future. But, once the technology is in place, how accepting will humans be to autonomous machines performing tasks traditionally performed by humans? Are we designing and developing robots that are human centric? In a study involving 357 undergraduate students, we found that acceptance of robots was dependent upon previous exposure to different forms of technology (i.e., robots, avatars, video games). Men were more likely to have previous exposure to technology, and were therefore more likely to accept robots and avatars in different tasks compared to women. Enhancing the acceptability of robots by both men and women will require an increased exposure to technology, and women may require additional experience with technology to close the technology acceptance gap.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".