Highly Cited Papers in Sport Sciences: Identification and Conceptual Analysis
Bibliographic record
Abstract
Highly cited papers reflect the top 1% of field and publication year papers. Highly cited papers are important in terms of the number of citations they receive in their subject area and often attract the attention of most researchers in terms of their high quality. Therefore, this study aimed to analyze highly cited papers in the field of sport sciences from a bibliometric perspective and to identify subject areas that have the potential to be highly cited. This research analyzed highly cited papers in the field of sport sciences published during 2010-2020, indexed in the Web of Science of the Clarivate Analytics. The results show that most of the highly cited papers in sport sciences are in sport medicine and published by prominent and renowned researchers. Moreover, most of these papers were contributed by researchers from the European and American continents. The results also show that the United States of America (USA), McMaster University of Canada, and Professor Lars Engebretsen led in publishing highly cited papers in sport sciences. It can be concluded that five thematic clusters were formed by highly cited papers in sport sciences, most of which were in the subject area of sport injuries and exercise physiology. Only highly cited papers in the field of sport sciences were analyzed, and a thorough analysis of all papers in this field is needed for a definite conclusion. This study identifies that the subject area has a great impact on a paper to be highly cited, and only some subject areas in the discipline of Sport Sciences have the potential to be highly cited.
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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 | 0.025 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.095 | 0.105 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".