Global Research Trends on Prosocial Behaviour: A Bibliometric Analysis
Bibliographic record
Abstract
This article describes a quantitative investigation of prosocial behaviour (PSB) research in bibliometric analysis. This bibliometric study focuses on the global research trends followed using Scopus Database. The main variables used for analyses of this study were by year, source, affiliation, author, country, area of a subject, and document type. The analysis of CiteScore, total publications, total citations, and h-index was done to rank the top contributors. The first research article on PSB was published in 1967, followed by the most recent publications in 2020. A total of 3,644 publications have been found during these 53 years. The author keywords and co-occurrences have been represented by bibliometric maps using VOSviewer 1.6.16. This study found an increase in the research trend for PSB, which was mainly published in seven (7) different publishers' journals. Amongst these, American Psychological Association, Wiley, and Frontiers are the three top publishers with 4.61%, 4.23%, and 4.21% contribution to the total publications. Findings regarding the top 15 most prolific authors showed that most of the authors related to PSB were from the United States, followed by Italy, Canada, and only one from Netherlands, Germany, and Chile. Further, most of the PSB research work was done in psychology. Overall, this study provides an evidence base, highlighting global trends and directions of research work published on PSB, adding value to the existing body of knowledge, and paving the way for future researchers.
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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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.150 | 0.199 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".