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Record W2172076658 · doi:10.2190/ec.41.1.e

The Socioemotional Effects of a Computer-Simulated Animal on Children's Empathy and Humane Attitudes

2009· article· en· W2172076658 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Educational Computing Research · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsEmpathySocioemotional selectivity theoryPsychologyFeelingAssociation (psychology)Developmental psychologySocial psychologyClinical psychologyPsychotherapist

Abstract

fetched live from OpenAlex

This study investigated the potential of using a computer-simulated animal in a handheld virtual pet videogame to improve children's empathy and humane attitudes. Also investigated was whether sex differences existed in children's development of empathy and humane attitudes resulting from play, as well as their feelings for a virtual pet. The results showed that after playing Nintendogs for 3 weeks, the participants of both sexes, on average, scored higher levels of empathy on the Bryant Empathy Index, and had higher levels of humane attitudes on the Intermediate Attitude Scale, compared to their pretest scores before they played. A statistical association also was revealed between time playing with a computer-simulated animal and improved scores in empathy and humane attitudes toward animals. The findings also showed that participants tended to form emotional attachments with their virtual pet and considered it a real pet.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.778
Threshold uncertainty score0.305

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.444
Teacher spread0.413 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it