The Benefits and Harms of Workplace Gossip: Considering its Valence, Content, and Target
Bibliographic record
Abstract
Workplace gossip, defined as the evaluative talk initiated by one employee (gossiper) to another (recipient) about an absent colleague, is widespread. Research indicates that over 90% of employees engage in gossip. Despite recent advancements in research on workplace gossip, two key challenges persist, hindering a comprehensive understanding of its harms and benefits. First, recent work tends to focus on the impacts on gossipers, with less known on how and why gossip may affect other stakeholders including gossip targets, gossip recipients, work groups, or the organization. Second, while there is an acknowledgment of the importance of differentiating gossip based on valence (positive vs. negative gossip), content (work vs. non-work-related gossip), and target (gossip about the supervisor vs. coworkers), it remains a theoretical and empirical challenge to encompass all these dimensions in a single study. This symposium aims to address these two critical issues by presenting four papers that contribute to a more holistic understanding of the harms and benefits of workplace gossip. These papers not only expand our insights into the consequences for gossipers but also delve into the impact on gossip recipients, work groups, and the organization. Moreover, the four papers employ novel theoretical perspectives and empirical methods to allow the simultaneous examination of multiple dimensions of gossip and their integrative impacts. Negative Gossip Undermines Organizational Diversity Efforts Author: Seval Gündemir; Rotterdam School of Management, Erasmus U. Author: Michael Slepian; Columbia Business School Author: Floor Rink; U. of Groningen Author: Bianca Beersma; Vrije U. Amsterdam Caught in the Web of Whispers: Emotional and Interpersonal Implications of Receiving Gossip Author: Rui Zhong; Penn State Smeal College of Business Author: Stephen Lee; Washington State U. Author: Yingxin Deng; School of Management, Beijing Institute of Technology, Beijing A Person-Centered View of Workplace Gossip Author: Qinglin Zhao; Texas A&M U. Author: Huiwen Lian; Texas A&M U. Author: Samantha Jordan; U. of North Texas Author: Yufan Deng; Southwestern U. of Finance and Economics Author: Wayne Hochwarter; Florida State U. Division and Solidarity: A Faultline Perspective of Workplace Gossip Author: Stephen Lee; Washington State U.
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 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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".