Social Robots: Views of Staff of a Disability Service Organization
Bibliographic record
Abstract
Social robotics is an emerging field, with many applications envisioned for people with disabilities. This project examined the so far invisible views of disability service organization workers towards social robotics. Because community service workers’ views shape community-based rehabilitation (an area of health interventions that focuses on social determinants), it is important to examine their views towards social robotics applications which are largely developed under a clinical/medical view of disability. We administered a survey to employees of a Saskatchewan disability service organization. Out of 44 respondents, 80 % were female, most aged 21–65 years. Robotics applications perceived to be important included domestic robots, and rehabilitation robots. Least important applications included eldercare robots, companion robots, and pet robots. Most participants felt that robots cannot replace human touch, human interaction, or emotional companionship, and that they cannot/should not replace human workers in the disability setting. Many expressed concerns about safety, normality for disabled people, and artificial interactions. Respondents also had views on whether a social robot can be a bully or could be bullied. We submit that the perspectives our respondents exhibited might be useful to consider in the development of social robots for applications around disability in order to ensure acceptable and relevant products.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".