ANTRENMAN VE EGZERSİZ YAYINLARININ GELİŞİMİ: 1980-2021 DÖNEMİNDE KÜRESEL ÜRETKENLİK VE YAYIN EĞİLİMLERİ
Bibliographic record
Abstract
Despite the increase in the number of global studies on training and exercise, which have an important place in sports sciences, there are still no bibliometric studies in the literature. This study aims to analyse the scientific articles that have been published on training and exercising by using bibliometric methods. Articles on training and exercise published between 1980 and 2021 were downloaded from the Web of Science (WoS). Spearman correlation coefficient was used for the correlation analysis between the number of articles and some development indicators of the countries. The exponential smoothing was used to estimate the number of articles to be published in the next years. Network visualization maps were used to identify citation analyses and trending topics. A total of 37408 articles were analysed. The top 3 contributing countries to the literature were USA (n=13227), UK (n=4481), Canada (n=3211). The most active journals were Journal of Applied Physiology (n=4338), Medicine and Science in Sports and Exercise (n=3292), Journal of Strength and Conditioning Research (n=2743). The top 3 most active institutions were University of California System (n=696), University of Copenhagen (n=678), University of North Carolina (n=644). The most active contributor to the literature was William J. Kraemer (Number of articles=223). We shared a summary of 37408 articles in this comprehensive bibliometric study on training and exercise. The topics studied in the last decade were determined as resistance training, football, athletic performance, high-intensity interval training, sports, youth, team sports, training load, injury prevention, health, quality of life, exercise therapy, obesity, aerobic exercise, muscle strength, biomechanics, balance, gait, heart rate variability, and hypertension.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".