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Record W2111092161 · doi:10.14198/jopha.2010.4.3.02

Children’s relationships with robots: robot is child’s new friend

2010· article· en· W2111092161 on OpenAlexaboutno aff
Meghann Fior, Sarah Nugent, Tanya Beran, Alejandro Ramirez‐Serrano, Roman Kuzyk

Bibliographic record

VenueJournal of Physical Agents (JoPha) · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsnot available
Fundersnot available
KeywordsFriendshipRobotPsychologySet (abstract data type)Task (project management)Block (permutation group theory)Developmental psychologySocial psychologyArtificial intelligenceComputer scienceEngineeringMathematics

Abstract

fetched live from OpenAlex

The purpose of this study was to examine how children think about and attribute features of friendship to a robot after a brief interaction with one. Children visiting a science centre located in a major Western Canadian city were randomly selected to participate in an experiment set up at the centre. A total of 184 children ages 5 to 16 years (M = 8.18 years) with an approximate even number of boys and girls participated. Children were interviewed after observing a traditional robot, a small 5 degree of freedom robot arm, perform a block stacking task. Content analysis was used to examine responses to nine open-ended questions. Results indicate that the majority of children were willing to engage in friendship with the robot by showing positive affiliation and social support towards it, as well as sharing activities, and communicating with it. Significant sex differences in how children ascribe characteristics of friendship to a robot were also found.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.286
Teacher spread0.264 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations42
Published2010
Admission routes1
Has abstractyes

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