Reputations at Work: Origins and Outcomes of Shared Person Perceptions
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".