University Rankings in Sport Science: A True Reflection of Excellence?
Bibliographic record
Abstract
BACKGROUND: University rankings often serve as benchmarks for excellence in academic evaluation. For example, ShanghaiRanking data from 2016 to 2023 for the Global Ranking of Sport Science Schools and Departments reveal consistent dominance by Australia (23.1%), Canada (18.0%), and the United States (12.0%), collectively counting over half of the top 50 universities worldwide. Nevertheless, there is uncertainty about how the methodology behind these rankings shapes a reality as much as it reflects one. PURPOSE: Our intention is to discuss the complexity of university rankings, using ShanghaiRanking as an example, to highlight how these rankings reflect academic excellence within the field of sport science. Current Evidence: When ranking universities in sport science, several aspects of academic excellence could be considered in addition to research metrics currently considered in ShanghaiRanking (publication, citation, citations per publication, top 25% journal publications, and internationally collaborated publications). These aspects may include (1) teaching quality, (2) practical training, (3) industry links and employability, (4) support services, (5) facilities and equipment, (6) international network, (7) community engagement, (8) sustainability and ethical practices, and (9) interdisciplinary approach. Altogether, they could provide a more comprehensive view of the quality and effectiveness of a sport-science program. CONCLUSIONS: The ranking of sport-science institutions poses a complex challenge due to diverse factors influencing academic excellence. Engaging in a broader dialogue and refining internationally relevant evaluation methodologies are crucial. These steps enable comparability between countries and provide a holistic understanding of the multidimensional nature of academic excellence in sport science.
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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 | 0.085 | 0.272 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.020 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".