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Record W2955730390 · doi:10.1145/3328320.3328377

Designing Picturebook Apps

2019· article· en· W2955730390 on OpenAlexafffund
Eric M. Meyers, Lisa P. Nathan, Bonnie Tulloch

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsNarrativeNegotiationWeavingValue (mathematics)Grounded theoryCultural heritageInteraction designSociologyVisual artsComputer scienceArtAnthropologyQualitative researchHuman–computer interactionEngineeringLiteratureHistorySocial science

Abstract

fetched live from OpenAlex

This article examines picturebook apps as sites of value negotiation and design in support of community and culture, illustrated through the ongoing design and development of an O-Pipon-Na-Piwin Cree community-centred picturebook app, Pīsim Finds Her Miskanow (Pīsim Miskam Omiskanow). We introduce the C&T community to early research on electronic picturebooks, and illustrate how picturebook theory is expanded with the remediation of print texts into multimodal narrative experiences. We turn to the case of Pīsim to further enrich and diversify these conceptions, weaving elements of culture into earlier models of picturebook apps. We draw on design theory, specifically the concept of values as hypotheses, to offer storied insights on how the design process of the Pīsim picture book app is enriched through its commitment to the originating community. We conclude with implications for the community-grounded design of interactive cultural belongings, not limited to picturebooks, including museum exhibits, educational installations, and digitized cultural heritage pieces.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.013
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.003

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 head0.011
GPT teacher head0.238
Teacher spread0.227 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations3
Published2019
Admission routes2
Has abstractyes

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