The Complexity of Workplace Gossip
Bibliographic record
Abstract
Workplace gossip, or evaluative talk by one employee (gossiper) to another (recipient) about an absent colleague (target), is ubiquitous (Foster, 2005). It is not surprising that increasing research effort has focused on it (Sun, Schilpzand, & Liu, 2022). Although prior research has generated valuable insights, our knowledge on workplace gossip remains incomplete. This is primarily because the phenomenon per se is complex. First, by definition there are at minimum three roles involved in workplace gossip – gossiper, recipient, and target. Different roles may have different attitudes and behaviors in the same episode of workplace gossip. Investigating gossip from different roles’ perspectives, therefore, is necessary for us to comprehensively understand this phenomenon. Second, theory on gossip is traditionally surrounded by debates. For instance, some scholars regarded gossip as bad and immoral behavior (Peters & Kashima, 2013), while others explored the benefits gossip brought to the collective (Feinburg, Willer, Stellar, & Keltner, 2012). Scholars should identify the debates in the literature, and address them by examining contingencies, integrating paradoxical views, and so on. Third, gossip continuously happens in interpersonal interactions at work. Even the same person’s attitudes and behaviors in gossip may change with time and context. Therefore, we may not be able to understand the complexity of workplace gossip without considering temporal and contextual factors associated with it. As stated below, the four studies in our symposium (1) focus on different roles’ perspectives, (2) address debates in the literature, and (3) examine the contextual factors associated with workplace gossip in rigorously designed studies. Paper 1: How is Gossip Viewed in the Eye of the Recipient Author: Yimin He; U. of Nebraska, Omaha Author: Zitong Sheng; Curtin U. Author: Sudong Shang; Griffith Business School, Griffith U. Author: Minghui Wang; Henan U. Paper 2: Negative Workplace Gossip toward the Supervisor and Gossiper Sender’s Well-Being. Author: Dan Ni; School of Business, Sun Yat-sen U. Author: Lindie Hanyu Liang; Wilfrid Laurier U. Author: Midori Nishioka; Wilfrid Laurier U. Author: Xiaoming Zheng; Tsinghua U. Author: Douglas J. Brown; U. of Waterloo Author: Elana Zur; Wilfrid Laurier U. Paper 3: Attack or Repair? Unpacking Employees’ Mixed Responses to Perceived Negative Gossip Author: Rui Zhong; Sauder School of Business, U. of British Columbia Author: Stephen Lee; The Wharton School, U. of Pennsylvania Author: Robin Mengxi Yang; School of Economics and Management, U. of Chinese Academy of Sciences Paper 4 Gossip and Its Change During an Event of Layoff: Does Perceived Layoff Justice Matter? Author: Jie Li; Wilfrid Laurier U. Author: Huiwen Lian; Texas A&M U. Author: Qinglin Zhao; Texas A&M U. Author: Yuhuan Xia; Shandong U. Author: Cynthia Lee; Northeastern U. Author: Chenduo Du; U. of Kentucky
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".