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Record W2012611415 · doi:10.1145/1268517.1268543

Location-dependent information appliances for the home

2007· article· en· W2012611415 on OpenAlexaffvenue
Kathryn Elliot, Mark Watson, Carman Neustaedter, Saul Greenberg

Bibliographic record

VenueProceedings · 2007
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsExploitContext (archaeology)Computer scienceHuman–computer interactionHome automationEthnographyContext awarenessWorld Wide WebMultimediaInternet privacyComputer securityGeographyTelecommunications

Abstract

fetched live from OpenAlex

Ethnographic studies of the home revealed the fundamental roles that physical locations and context play in how household members understand and manage conventional information. Yet we also know that digital information is becoming increasingly important to households. The problem is that this digital information is almost always tied to traditional computer displays, which inhibits its incorporation into household routines. Our solution, location-dependent information appliances, exploit both home location and context (as articulated in ethnographic studies) to enhance the role of ambient displays in the home setting; these displays provide home occupants with both background awareness of an information source and foreground methods to gain further details if desired. The novel aspect is that home occupants assign particular information to locations within a home in a way that makes sense to them. As a device is moved to a particular home location, information is automatically mapped to that device along with hints on how it should be displayed.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.268
Teacher spread0.254 · 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 designSimulation or modeling
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

Citations20
Published2007
Admission routes2
Has abstractyes

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