From “Made In” to Mukokuseki: Exploring the Visual Perception of National Identity in Robots
Bibliographic record
Abstract
People read human characteristics into the design of social robots, a visual process with socio-cultural implications. One factor may be nationality, a complex social characteristic that is linked to ethnicity, culture, and other factors of identity that can be embedded in the visual design of robots. Guided by social identity theory (SIT), we explored the notion of “mukokuseki,” a visual design characteristic defined by the absence of visual cues to national and ethnic identity in Japanese cultural exports. In a two-phase categorization study ( \(n=212\) ), American ( \(n=110\) ) and Japanese ( \(n=92\) ) participants rated a random selection of nine robot stimuli from America and Japan, plus multinational Pepper. We found evidence of made-in and two kinds of mukokuseki effects. We offer suggestions for the visual design of mukokuseki robots that may interact with people from diverse backgrounds. Our findings have implications for robots and social identity, the viability of robotic exports, and the use of robots internationally.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".