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Record W2403646712

Assistive Technologies and Children-Robot Interaction.

2007· article· en· W2403646712 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

VenueNational Conference on Artificial Intelligence · 2007
Typearticle
Languageen
FieldPsychology
TopicSocial Robot Interaction and HRI
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsHuman–computer interactionModalitiesMobile robotMultitudeHumanoid robotVariety (cybernetics)RobotComputer scienceAutismSocial robotMobile interactionArtificial intelligenceRobot controlPsychologyMobile deviceDevelopmental psychologyWorld Wide Web
DOInot available

Abstract

fetched live from OpenAlex

Mobile robots are machines that can move and act in the real world, making it possible to generate a multitude of interplay situations, which can engage children and encourage interaction in a variety of different ways. Mobility, appearance, interaction modalities (e.g., sound, light, moving parts) and behaviour (predetermined and adaptive) can all have an influence in sustaining the interest (and therefore learning) of typically developing children or children with specific deficits such as autism. This paper summarizes findings from two of our on-going projects, one using a spherical mobile robot and the other using a humanoid-like robot toy.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.815
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.001

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 head0.181
GPT teacher head0.445
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