Mapping the Intellectual Landscape of Workplace Incivility: A Bibliometric Analysis
Bibliographic record
Abstract
This bibliometric study examines the evolution of research on workplace incivility from 1999 to 2025. Using a curated dataset of 1,000 scholarly articles retrieved through the Publish or Perish software from Google Scholar, we analyze multiple bibliometric indicators, including annual publication trends, citation performance, the most prolific authors, top contributing institutions, leading journals, and patterns of international collaboration. Results reveal steady growth in scholarly output, accompanied by a significant surge in research activity after 2010. Peak productivity was recorded in 2021, with 103 publications. The most-cited paper in the dataset is Andersson and Pearson (1999), Tit for Tat? The spiralling effect of incivility in the workplace, which has received 5,402 citations, underscores the influence of foundational theories in the field. The United States, Canada, and the UK emerged as major research hubs, with growing contributions from Asian and European scholars in recent years. This paper provides a comprehensive guide for academicians, practitioners, and policymakers aiming to understand the trajectory of academic progress and anticipate future directions in this evolving field.
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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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.089 | 0.133 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".