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Record W4319317815 · doi:10.55612/s-5002-054-009

COVIDware: Designing Interactive Everyday Things as Tangible Homeware for Social Isolation

2022· article· en· W4319317815 on OpenAlexaff
Alaa Nousir, Renee Chen, Anne Liu, Meara Donovan, Eliza Wallace, Lee W. Jones, Sara Nabil

Bibliographic record

VenueInteraction design & architecture(s)/ID&A Interaction design & architecture(s) · 2022
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsQueen's University
Fundersnot available
KeywordsIsolation (microbiology)Process (computing)Everyday lifeHuman–computer interactionComputer scienceInteraction designDesign processInteractive designInteractive artEngineeringWork in processArt

Abstract

fetched live from OpenAlex

This paper describes our collaborative journey of creating everyday interactive artefacts to help us think, reflect, and live through self-isolation. Through a co-design approach, we designed interactive homeware objects (that we collectively refer to as ‘COVIDware’) to address the challenges of isolation during the pandemic. Five artefacts were developed by self-isolated designers as interactive art installations. We discuss how each creator reflected on her design concept, process, and encounter through concepts of critical making, speculation, and engagement via in-the-isolated-wild deployments. By empowering early researchers/enthusiasts to design ‘with’ smart-materials, and off-the-shelf items, we reflect on how these homey interfaces can enhance people’s wellbeing beyond screen-based interactions. Despite not collaborating in the making process, our findings from the designer’s making process show how all the designed artefacts shared attributes of biophilic design, imperfection, and unconventional interactions with the overarching goal of promoting wellbeing, and meaningful connection with nature, self, and others.

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 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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.797
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0050.000
Scholarly communication0.0010.005
Open science0.0030.001
Research integrity0.0010.006
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.307
Teacher spread0.265 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
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

Citations2
Published2022
Admission routes1
Has abstractyes

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