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Record W2800485607 · doi:10.1002/jcpy.1051

Urgently Yours: Temporal Communication Norms and Psychological Distance

2018· article· en· W2800485607 on OpenAlexafffund
Alex Kaju, Sam J. Maglio

Bibliographic record

VenueJournal of Consumer Psychology · 2018
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsModalitiesMediationModerationNormativeInterpretation (philosophy)Variety (cybernetics)PsychologyModality (human–computer interaction)Through-the-lens meteringSpace (punctuation)Field (mathematics)Social psychologyClosure (psychology)Cognitive psychologyComputer scienceLens (geology)SociologyEpistemologyHuman–computer interactionArtificial intelligencePolitical scienceMathematics

Abstract

fetched live from OpenAlex

Communicators have at their disposal an ever‐increasing variety of modalities by which to transmit messages. Do the prominent norms attached to different communication modalities contain information in and of themselves? The present investigation considers this question through the lens of text messages and emails, hypothesized to confer different normative information about urgency. Text messages (vs. emails)—or any communication inferred by receivers to have been sent with greater urgency—cause the content of those messages to seem, in the eyes of receivers, closer in time (Study 1), closer in space (Study 2), and more likely to occur (Study 3). Moderation and mediation analyses confirm the role of urgency underlying the relationship between modality and distance. We provide evidence for these effects in a series of laboratory studies together with a field study in which real text messages and emails impact not only inferred distance but also a behavior deriving therefrom. Theoretical and practical implications highlight the importance, for both researchers and practitioners, of understanding how the medium of communication can shape interpretation of the message.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.282
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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.174
GPT teacher head0.459
Teacher spread0.285 · 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 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

Citations26
Published2018
Admission routes2
Has abstractyes

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