How to Help Robots Take Over the World (in a Good Way): Media Influence on the Acceptance of New Technology
Bibliographic record
Abstract
Past research has established that since robots are not yet common in Western society, most people’s views are based on how they are represented in science fiction versus current reality (Bruckenberger et al., 2013; Teo, 2021). This is the foundational problem for robots as a unique technology where common understanding is based on predisposed stereotypes from decades of pop culture, which can represent an impediment to acceptance. This paper acts as a case study on how media can be leveraged to influence audience acceptance of new technology such as social robots. I evaluate this by measuring the baseline of attitudes towards robots, probing the sources of these beliefs including how they are informed by pop culture, then subsequently quantifying how exposure to nonfiction media can reshape these embedded notions. The goal is to test if there is a lift in positive sentiment when shown marketing videos of an existing robot Pepper by Softbank Robotics. This study had mixed results revealing there is a high degree of skepticism in audiences regarding robots which is a hurdle that must be addressed and overcome to effectively integrate into future society. This MRP is a first account at developing insights for companies and governments on how to successfully drive acceptance of this new technology to facilitate successful HumanRobot Interaction (HRI). The goal is to situate HRI at the intersection of media theory, to analyze diffusion of innovation from a perspective of audience, influence, and acceptance.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.000 | 0.002 |
| 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".