Highly Cited Papers in Sport Sciences: Identification and Conceptual Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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 it