A bibliometric analysis of verbal harassment
Bibliographic record
Abstract
The primary objective of this study is to systematically examine the scholarly literature on verbal harassment. Using VOSviewer, a dataset of 145 records was extracted from the Scopus database for this investigation. The articles were organized by year, publication, author, co-author nation, affiliation, keywords, and journal title. Furthermore, they were analyzed based on several key parameters, including country contributions, leading institutions and authors, journal distribution, the most-cited articles, bibliographic coupling, and keyword analysis. This study identifies various research clusters, including discrimination, transgender issues, victimization, and Asian Americans; violence, sexual violence, and transphobia; as well as bullying, youth, school environment, and LGBTQ. Additionally, the United States, the United Kingdom, and Canada were found to be the leading contributors to publications on verbal harassment between 1967 and 2022. The University of Minnesota, the University of California at Davis, and New York University emerged as the top three institutions with the highest number of citations in published articles. In contrast to prior bibliometric studies that have primarily focused on cyberbullying, school-based bullying, or gender-based online violence, this study presents a broader, longitudinal, and multidisciplinary mapping of verbal harassment research across various social contexts. It provides a critical foundation for future cross-disciplinary research and the development of policy framework addressing verbal aggression.
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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 | 0.011 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.288 | 0.349 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".