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Record W2125034970 · doi:10.5555/1771592.1771599

Yours, mine and ours? sharing and use of technology in domestic environments

2007· article· en· W2125034970 on OpenAlexaff
A. J. Bernheim Brush, Kori Inkpen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsDalhousie University
Fundersnot available
KeywordsSituatedUbiquitous computingComputer scienceInternet privacyWorld Wide WebHuman–computer interaction

Abstract

fetched live from OpenAlex

Abstract. Domestic technologies have been a popular area of study for ubiquitous computing researchers, however there is relatively little recent data on how families currently use and share technologies in domestic environments. This paper presents results from an empirical study of 15 families in the U.S in early 2007. We examined the types of technologies families own, including TVs, music players, phones and computers; where they were situated within the home; and the degree of shared ownership and use. Our results call attention to the prevalence of shared usage of technology in domestic environments and also suggest opportunistic spaces for ubiquitous computing technology. While not all ubiquitous computing technologies for domestic environments will be shared, the diverse ways families chose to share their computers suggest that future devices might better match how families wish to use shared technology by supporting both the shared usage model of appliances and the ability to access a personal profile.

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)0.002
metaresearch head score (Gemma)0.008
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0030.003
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.023
GPT teacher head0.282
Teacher spread0.259 · 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
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

Citations85
Published2007
Admission routes1
Has abstractyes

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