An Irish ‘Traveling’ Air-Purification Robot in a Care Home in Tokyo: Why Do Humanities and Social Science Matter?
Bibliographic record
Abstract
This paper examines the adoption process of an Irish air-purification robot in a nursing home in Japan. For the three-year project, researchers and engineers, interested in exploring human-robot interactions, formed a transdisciplinary team. Stemming from the concept of user-centered design, one original air-purification robot was produced and tested in one care facility each in Ireland and Japan. The robot ‘traveled’ from Dublin to Tokyo, and spent three months working in a residential care home where its interactions with users were observed. Based on ‘digital technography’ and ‘matters of concern’, the paper describes the research processes, treating this as a journey of the robot, and how it was imagined (at the design stage in Ireland) and reimagined in a different cultural context (at the point of use in Japan). The paper refers to challenges that we as transdilsciplinary researchers encountered along the way, while emphasizing the significance of deciphering the human ‘context’ in which AI and robotics are applied. The paper seeks to answer why humanities and social sciences research matter greatly.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".