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Reputations at Work: Origins and Outcomes of Shared Person Perceptions

2023· article· en· W4388662589 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

VenueAnnual Review of Organizational Psychology and Organizational Behavior · 2023
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWork (physics)PerceptionPsychologySocial psychologyEngineering

Abstract

fetched live from OpenAlex

Reputations are immensely consequential for both people and organizations. Yet research on reputations in the workplace is fragmented across a number of literatures. In this article, we first review conceptual and definitional issues surrounding the study of reputations in the workplace. We then summarize several theoretical frameworks for studying reputations drawing from the literature on accuracy and errors in person perception, surveying the Realistic Accuracy Model, Self-Other Knowledge Asymmetry model, impression management, socioanalytic theory, social cognition, stereotypes, gossip, and culture. We present the Trait-Reputation-Identity model as a framework for integrating these disparate literatures. Next, we discuss broad areas where workplace reputations may impact individual and organizational outcomes including job performance, career success, and well-being. We conclude by offering a number of observations regarding the state of the literature on reputations and prospects for contributing to organizational psychology and organizational behavior.

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 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 score0.043
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.000

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 head0.049
GPT teacher head0.401
Teacher spread0.352 · 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