Characteristics of Articles Published in Different Quartiles: A Bibliometric Analysis of Sport and Exercise Psychology Journals
Bibliographic record
Abstract
Background. The purposes of this study were to determine the quantitative characteristics of the articles published in sports and exercise psychology journals in four quartiles of Web of Science and to understand whether these characteristics had any effect on citation. Methods. Sport and exercise psychology journals indexed under the Sport Sciences subject category were included in the study. A total of 172 articles published in 2017, which was the most recent date meeting inclusion criteria, were collected from four sport and exercise psychology journals included: Psychology of Sport and Exercise (Q1), Journal of Sport and Exercise Psychology (Q2), Sport Psychologist (Q3), and International Journal of Sport Psychology (Q4). Results. The articles published in Q1 journal were multi-institutional and multinational. Besides, they had longer abstracts and discussions and cited more references. The results revealed that multinational articles and articles with structured abstracts had higher citations than single country articles and articles with unstructured abstracts. The number of affiliated countries and references, and longer titles, abstracts, and introductions were found significantly associated with the number of citations. The bibliometric analysis indicated that the UK, the USA, and Canada were the major contributing countries. Conclusion. This study is the first attempt to investigate the characteristics of articles published in different WoS quartiles within the field of sports and exercise psychology. The findings of the study may help researchers to improve their citation count. Keywords: scientific productivity, essential science indicators, journal impact factor, Web of Science, scientometrics.
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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 | 0.010 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.106 | 0.107 |
| Science and technology studies | 0.001 | 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".