Interacting with a Computer-Simulated Pet: Factors Influencing Children's Humane Attitudes and Empathy
Bibliographic record
Abstract
Previous research by Tsai and Kaufman (2010a, 2010b) has suggested that computer-simulated virtual pet dogs can be used as a potential medium to enhance children's development of empathy and humane attitudes toward animals. To gain a deeper understanding of how and why interacting with a virtual pet dog might influence children's social and emotional development, this study gathered detailed data regarding the effects of play duration and types of play interaction on the enhancement of children's empathy and humane attitudes. Quantitative findings revealed that participants who spent more time playing with their virtual pet tended to have higher humane attitude scores. In addition, play interaction that involved competing with a virtual pet dog was associated with higher empathy and humane attitude scores, and the reported number of incidents of care provided was associated with higher humane attitude scores. Qualitative findings showed that the participants built an emotional bond with their virtual pet dogs and that many participants believed that the virtual dogs had their own interests and personalities. Many participants tended to base activities they would like to do with their virtual pet dogs on what they perceived to be the virtual pet dog's interests and needs.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".