Bibliometric analysis of harassment and bullying in sport
Bibliographic record
Abstract
Background and Study Aim. This study aims to chart the trend of publications in the Scopus database and the Web of Science as well as the global evolution of harassment and bullying in sports. Material and Methods. ScientoPy and VOSviewer software were used in this study to analyze the number of publications, well-known research topics, proactive authors, author keywords, preferred sources, and institutional data. This study uses data reconciliation with 1, 883 different items from the Scopus database and the Web of Science. An increasing trend in sports nutrition research was found using the Scopus and WoS databases. Results. This field has grown significantly since 2015. In addition, the percentage of documents published in the last two years (2020 to 2021) shows that 22.40% were published on WoS and 22.04% on Scopus. The five keywords that are trending topics between 2020 and 2021 are "Sports", "Racism", "Race", "Sexual Harassment", and "Gender". Meanwhile, the keywords with the highest total link strength were "sports" (244), "racism" (169), "bullying" (165), "adolescents" (161), and "physical activity" (150). The University of Toronto, Canada, became the most productive institution with 22 publications. Conclusions. The most prestigious institutes and researchers in the field of harassment and bullying research in sports have been recognized, along with key research areas, keywords, and related papers. The study also offers potential readers and researchers a global perspective on the hottest issues in harassment and bullying in sport today. In addition, it provides various analyses to assist in the organization of data for the development of harassment and bullying research theories and methodologies in strong sports.
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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 | 0.008 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.145 | 0.210 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".