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Record W3103663076 · doi:10.1002/9781119057840.ch45

Accurate Interpersonal Perception

2020· other· en· W3103663076 on OpenAlexaff
Lauren J. Human

Bibliographic record

VenueThe Wiley Encyclopedia of Health Psychology · 2020
Typeother
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsMcGill University
Fundersnot available
KeywordsPerceptionPsychologyInterpersonal communicationInterpersonal relationshipInterpersonal perceptionSocial perceptionSocial psychologyPhysical healthPsychological healthDevelopmental psychologyMental healthClinical psychologyPsychotherapist

Abstract

fetched live from OpenAlex

Social relationships play an important role in psychological and physical health, yet the specific relationship processes that underlie these broader associations are not well understood. One factor that may play an important role is the accuracy of interpersonal perceptions: the extent to which an individual's states and traits are accurately perceived. In particular, both viewing others accurately and being viewed accurately may benefit social, psychological, and physical functioning. Indeed, there is growing evidence that more accurate perceptions in first impressions and close relationships are related to positive social outcomes and better psychological health for perceivers and even more so for targets. Further, there is preliminary evidence that greater accuracy and accuracy-related processes, such as self-verification and expressivity, are associated with better physiological functioning. Overall, the accuracy of interpersonal perceptions may play an important role linking social processes to downstream physical health outcomes, presenting a critical area for future research.

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)0.003
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.002

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 head0.070
GPT teacher head0.434
Teacher spread0.364 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2020
Admission routes1
Has abstractyes

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