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Record W4396831648 · doi:10.1145/3613905.3651126

Passive Co-presence: Exploring How Peripheral Devices Connect People Over Distance

2024· article· en· W4396831648 on OpenAlexaff
Hanieh Shakeri, Carman Neustaedter

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsSoftware deploymentCo-designHuman–computer interactionFrame (networking)Computer scienceSet (abstract data type)Plan (archaeology)Field (mathematics)Assisted livingArchitectural engineeringAging in placeHome automationTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

When families live in the same home, they feel a sense of connection through the subtle, passive aspects of family life. Over distance, these passive aspects are hard to experience as most communication technologies support sharing conversations or activities. Using a co-design study, Research-Through-Design (RtD) methods, and a field deployment, I aim to explore the design of smart home technologies for passive co-presence over distance. The co-design study uncovered differences in the connection needs of emerging adults and their parents, and provided a set of design considerations including designing for the need for control and privacy, sharing multi-sensory environmental ambience, and supporting nostalgia and comfort. These findings guided an RtD exploration resulting in the design of two artifacts – the There Chair and Fragrance Frame. To understand the impact of integrating passive co-presence designs into the home, I plan to conduct a field deployment, which I describe in this work.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.003
Scholarly communication0.0030.004
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.039
GPT teacher head0.303
Teacher spread0.264 · 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 designObservational
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

Citations3
Published2024
Admission routes1
Has abstractyes

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