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Record W2951224846 · doi:10.1145/3322276.3323687

Diversifying the Domestic

2019· article· en· W2951224846 on OpenAlexaff
William Odom, Sumeet Anand, Doenja Oogjes, Jo Shin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsVisionMainstreamWork (physics)SociologyGenerative grammarEngineering ethicsPublic relationsComputer-supported cooperative workMobile technologyMobile devicePolitical scienceComputer scienceEngineering

Abstract

fetched live from OpenAlex

We report on a design research inquiry aimed at understanding and exploring the values, practices, and perspectives of people that actively embrace and choose to live within collective houses and mobile vehicles as their homes. A goal of our work is to inquire into how such lived alternatives of 'home' can take a step toward broadening possibilities for conceptualizing 'domestic' technology and provoking questions around how it might be critiqued, imagined, and designed. We offer a brief overview of our ongoing research with a sample of collective and mobile dwellers, and propose three themes that extend prior generative work in this area: critique through living, taking time to adapt, and the transitional home. We use these themes in a design-led approach to propose six fictional future technology concepts that aim to (i) critically reflect on and provoke questions about commitments in current mainstream visions of domestic technology and (ii) explore new possibilities for engaging with the material, social, and technological conditions shaping the lives of our collective and mobile dweller participants. We conclude with a reflection on our work, its limitations, and opportunities it suggests for future research and practice.

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)0.007
metaresearch head score (Gemma)0.006
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 score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0080.019
Scholarly communication0.0110.011
Open science0.0020.015
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0130.001

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 head0.015
GPT teacher head0.276
Teacher spread0.262 · 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

Citations18
Published2019
Admission routes1
Has abstractyes

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