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Record W1596326723 · doi:10.1109/icsmc.2003.1244338

Characteristics of mobile robotic toys for children with pervasive developmental disorders

2004· article· en· W1596326723 on OpenAlexafffund
Fran ̧ois Michaud, Audrey Duquette, Isabelle Nadeau

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsUniversité de Sherbrooke
FundersCanada Research Chairs
KeywordsAutismSocializationHuman–computer interactionComputer scienceRobotMobile robotPsychologyMultimediaDevelopmental psychologyArtificial intelligence

Abstract

fetched live from OpenAlex

Pervasive developmental disorders (PDD) refers to a group of disorders characterized by delays in the development of multiple basic functions including socialization and communication. Symptoms may include communication problems such as using and understanding language; difficulty relating to people, objects, and events; unusual play with toys and other objects; difficulty with changes in routine or familiar surroundings, and repetitive body movements or behavior patterns. Autism is the most characteristic and best studied PDD. We are investigating the use of mobile robotic toys that can move in the environment and interact in various manners (vocal messages, music, visual cues, movement, etc.) with children with autism. The hypothesis is that mobile robots can serve as an appropriate pedagogical tool to help children with PDD develop social skills because they are more predictable and less intimidating. The objective is to see how such devices can be used to capture the child's attention and contribute to helping him or her develop social skills. This paper outlines the design considerations for such robots, and presents experimental protocols that are being developed to study the impacts of using these robots on the development of the child.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.329
Threshold uncertainty score0.461

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.016
GPT teacher head0.264
Teacher spread0.247 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations65
Published2004
Admission routes2
Has abstractyes

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