Bibliometric Analysis of Sports and Gender Equality Studies in the Context of Sustainable Development Goals
Bibliographic record
Abstract
Academic studies indicate that the topic of sport and sustainability is increasingly taking a prominent role in social life and is accepted as a societal norm, since sustainable development efforts play a crucial role in enhancing social well-being, promoting gender-inclusive participation, and empowering women. Accordingly, examining research trends on these topics over time constitutes an important area of study. This study investigates the growing number of scientific publications on gender equality in sport, produced within the framework of the SDGs and published between 1992 and 2025, through a bibliometric analysis. A total of 801 research records were obtained from the Web of Science Core Collection and analysed with VOSviewer, focusing on productivity and collaboration networks across authors, universities, countries, and publishers. The findings highlight the dominance of the United States, followed by the United Kingdom, Australia, and Canada, and show that research has gained momentum since the 1990s, with a marked increase particularly after the United Nations announced the SDG vision in 2015. Moreover, the results reveal that conceptual focuses in the field of sport and gender equality have diversified, with themes such as “inclusivity” and “feminism” gaining prominence. By identifying gaps in the literature, this study offers strategic opportunities for emerging researchers and contributes to a deeper understanding of the role of sport in advancing gender equality goals within sustainable development. Keywords: bibliometric analysis, sports studies, sustainability, gender equality, SDGs
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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 | 0.018 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.190 | 0.304 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".