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Record W2921439555 · doi:10.1145/3294109.3300973

Baby Tango

2019· article· en· W2921439555 on OpenAlexafffund
Joanna Berzowska, Alex Mommersteeg, Laura Isabel Rosero Grueso, Eric Ducray, Michael Patrick Rabo, Geneviève Moisan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsConcordia University
FundersDivision of Materials ResearchConcordia UniversityConcordia University of Edmonton
KeywordsComputer scienceInteraction designHuman–computer interactionArtifact (error)Bridge (graph theory)Robustness (evolution)MultimediaArtificial intelligence

Abstract

fetched live from OpenAlex

We describe two prototypes from the Baby Tango project: electronic textile toys that enable soft, tangible, full-body interaction. It presents interaction techniques that bridge the physical, the digital, and the social, as well as a case study in constructing interactive composite textiles. Given that the softness of the toy is a central design constraint, most of the circuit, including the sensors, is embroidered directly on the surface of the artifact using technical threads (with varying electro-mechanical properties) and a digital embroidery/laying machine. This submission includes design and technical details, as well as initial interaction design scenarios. The next steps of this project will explore how these toys could support the development of empathy in toddlers through embodied play. Further work is needed in order to develop background research, collaborations with early childhood researchers, as well as empirical studies. Future work will include the development of these studies; iterating aspects of interaction and play through participatory design; and improving technical design to focus on reliability, robustness, and durability.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.898
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1020.021

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 head0.009
GPT teacher head0.243
Teacher spread0.234 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations12
Published2019
Admission routes2
Has abstractyes

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