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Record W1981438494 · doi:10.1145/1056808.1057026

OverHear

2005· article· en· W1981438494 on OpenAlexaff
David C. Smith, Matthew J. Donald, Daniel Chen, Daniel Cheng, Changuk Sohn, Aadil Mamuji, David Holman, Roel Vertegaal

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsQueen's University
Fundersnot available
KeywordsComputer scienceSpace (punctuation)Focus (optics)Face (sociological concept)Point (geometry)Human–computer interactionLinguistics

Abstract

fetched live from OpenAlex

One of the problems with mediated communication systems is that they limit the user's ability to listen to informal conversations of others within a remote space. In what is known as the Cocktail Party phenomenon, participants in noisy face-to-face conversations are able to focus their attention on a single individual, typically the person they look at. Media spaces do not support the cues necessary to establish this attentive mechanism. We addressed this issue in our design of OverHear, a media space that augments the user's attention in remote social gatherings through computer mediated hearing. OverHear uses an eye tracker embedded in the webcam display to direct the focal point of a robotic shotgun microphone mounted in the remote space. This directional microphone is automatically pointed towards the currently observed individual, allowing the user to OverHear this person's conversations.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.922
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.011
GPT teacher head0.262
Teacher spread0.251 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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

Citations9
Published2005
Admission routes1
Has abstractyes

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