Children’s Imaginaries of Robots for Playing With
Bibliographic record
Abstract
Abstract Children with severe motor impairment due to cerebral palsy have difficulties engaging in play, although they want to play games that typically developing children play. The barriers imposed by motor impairments against engaging in play can be addressed through the use of robots. We aim to identify how children, who have extensive experience of play, imagine what a robot is and what features would make a robot good to play with. Using a qualitative description design, 19 children from urban and rural settings participated in focus groups to draw and talk about the robots they would like to exist. The data were coded and analyzed using a summative approach to content analysis. The findings revealed that the children imagined that a good robot to play with is one that has an anthropomorphic appearance, is tough and strong, has controls, and that is able to move, grab, speak, and play popular children’s games. In particular, the girls imagined that robots should be able to express positive emotions towards children. Age, gender, culture, and the physical environment in which the children lived influenced what they expected to find in a robot for playing with and how they imagined child–robot interactions.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".