Bibliographic record
Abstract
This study aims to analyze cyberbullying research in Turkey through bibliometric analysis. To this end, the study dealt with six research questions which included the most frequently used keywords and co-occurrences of these keywords, Turkey’s collaboration with other countries, the frequency distribution of articles and citations by year, the most cited articles, the most productive journals, and authors. The bibliometric data were limited to the Web of Science (WoS) database. The first search yielded a total of 3974 publications. However, excluding the publications which did not comply with the aim of the study resulted in 105 articles to be analyzed. The findings suggested that there were 268 keywords used at least once. The keywords occurring at least five times other than “cyberbullying” were “cyber victimization,” “adolescent,” “bullying,” “reliability,” “validity,” and “internet addiction.”. Turkey had at least one collaboration with 21 countries. The top five countries with at least two collaborations were England, the U.S.A., Australia, Hungary, Czech Republic, and Germany. The most productive year was 2021. The most cited article was published in 2010, and the most influential journal was "Education and Science.” It was also revealed that the fifteen most productive authors had 57 publications. Considering the increasing interaction among people in virtual environments, cyberbullying research which has a nearly quarter-century history, should take more attention from Turkish scholars. Additionally, a gap was observed in the literature regarding studies conducted on parents. Thus, further studies may attempt to fill this gap.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.157 | 0.179 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".