Moving beyond assumptions of deviance: The reconceptualization and measurement of workplace gossip.
Bibliographic record
Abstract
Despite decades of research from other academic fields arguing that gossip is an important and potentially functional behavior, organizational research has largely assumed that gossip is malicious talk. This has resulted in the proliferation of gossip items in deviance scales, effectively subsuming workplace gossip research into deviance research. In this paper, the authors argue that organizational research has traditionally considered only a very narrow subset of workplace gossip, focusing almost exclusively on extreme negative cases which are not reflective of typical workplace gossip behavior. Instead of being primarily malicious, typical workplace gossip can be either positive or negative in nature and may serve important functions. It is therefore recommended that workplace gossip be studied on its own, independent of deviance. To facilitate this, the authors reconceptualize the workplace gossip construct and then develop a series of general-purpose English- and Chinese-language workplace gossip scales. Using 8 samples (including qualitative, multisource, multiwave, and multicultural data), the authors demonstrate the construct validity, reliability, cross-cultural measurement invariance, and acceptable psychometric properties of the workplace gossip scales. Relationships are demonstrated between workplace gossip and a variety of other organizational variables and processes, including uncertainty, emotion validation, self-esteem, norm enforcement, networking, influence, organizational justice, performance, deviance, and turnover. Future directions in workplace gossip research are discussed. (PsycINFO Database Record
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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 | 0.038 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.048 |
| Scholarly communication | 0.010 | 0.021 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.001 | 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".