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Record W2104010214 · doi:10.1145/2485760.2485866

Youtopia

2013· article· en· W2104010214 on OpenAlexafffund
Alissa N. Antle, Alyssa Friend Wise, Amanda Häll, Saba Nowroozi, Perry Tan, Jillian L. Warren, Rachael Eckersley, Michelle Fan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicICT in Developing Communities
Canadian institutionsSimon Fraser University
FundersNetworks of Centres of Excellence of CanadaSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceInterface (matter)NegotiationHuman–computer interactionMode (computer interface)MultimediaWorld Wide WebSociology

Abstract

fetched live from OpenAlex

Youtopia is a hybrid tangible and multi-touch land use planning activity for elementary school aged children. It was implemented on a Microsoft Pixelsense digital tabletop. The main method of interaction is through physical stamp objects that children use to "stamp" different land use types onto an interactive map. Youtopia was developed to investigate issues surrounding how to design and evaluate children's collaborative learning applications using digital tabletops. In particular we are looking at how the interface design supports in depth discussion and negotiation between pairs of children around issues in sustainable development. Our primary concern is to investigate questions about codependent access points, which may enable positive interdependence among children. Codependent access points are characteristics that enable two or more children to participate and interact together. In Youtopia these implemented through sequences of stamps that are required for successful interaction, which can be assigned to children (codependent mode) or remain unassigned (independent mode).

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)0.000
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.175
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1750.069

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 head0.013
GPT teacher head0.199
Teacher spread0.186 · 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

Citations64
Published2013
Admission routes2
Has abstractyes

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