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Record W1784048760 · doi:10.7939/r3dn3zx3d

Robot Enhanced interaction and learning for children with profound physical disabilities

2011· article· en· W1784048760 on OpenAlexaff
Mingyuan Meng, Kathy Howery, Jason Gu, Al Cook

Bibliographic record

VenueUniversity of Alberta Library · 2011
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsGlenrose Rehabilitation HospitalUniversity of Alberta
Fundersnot available
KeywordsTask (project management)Context (archaeology)Human–computer interactionPsychologySequence (biology)RobotComputer scienceArtificial intelligenceEngineeringBiology

Abstract

fetched live from OpenAlex

The goal of this study was to explore how children who have significant physical disabilities could use a robotic arm to interact in a play and exploration activity. These children cannot manipulate toys and other objects to engage in typical play activities with adults or their peers. A robotic arm was used to provide an alternative method to engage in joint play activities. Using the robotic arm, these children were able to engage in play with an adult. For successful play experiences, this activity required manipulation of objects in sequence and turn taking with the adult. Children were able to experience, independently, the mediated manipulation of real objects in the context of a play activity. They demonstrated an ability to interact and to carryout a sequence of steps to complete a play task.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.000
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 head0.040
GPT teacher head0.306
Teacher spread0.266 · 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 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

Citations36
Published2011
Admission routes1
Has abstractyes

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