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Isn't That Spatial?: Distance and Communication in a 2-D Virtual Environment

2006· article· en· W1974973871 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Computer-Mediated Communication · 2006
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsIntertek (Canada)Office of the Chief Medical Examiner
Fundersnot available
KeywordsAttractionInterpersonal communicationSocial distanceGeographical distanceInterpersonal attractionFunction (biology)Interpersonal relationshipSocial psychologyPsychologyDistance decayComputer scienceCommunicationMathematicsGeographySociologyCoronavirus disease 2019 (COVID-19)

Abstract

fetched live from OpenAlex

This article examines the spatial relationships between avatars (i.e., graphical identities or icons) over time in a 2-dimensional online chat environment. The Spatial Distance Analysis Program (SDAP) was developed to measure the distance between avatars in a specially designed Palace environment. Correlations between distance and interpersonal communication constructs of (1) conversational appropriateness, (2) social attraction, and (3) uncertainty reduction indicate that distance effects are significant in an online environment. Specifically, it was found that general conversational appropriateness mediated between uncertainty reduction and specific conversational appropriateness for individuals who moved closer together and farther apart over time, respectively. Furthermore, the relationship between social attraction and distance indicated a significant positive parabolic function; that social attraction (i.e., liking) decreased at middle distances and increased at low and high distances. This finding suggests that there are three interpersonal distance zones in online communication.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.443
Threshold uncertainty score0.488

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.187
Teacher spread0.181 · 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