Analysis of Research Hotspots and Evolutionary Pathways of the Olympic Movement

GAO Lulu, LIU Tianxing, LASZLO Vajda

Journal of Capital University of Physical Education and Sports ›› 2025, Vol. 37 ›› Issue (4) : 433-444.

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Journal of Capital University of Physical Education and Sports ›› 2025, Vol. 37 ›› Issue (4) : 433-444. DOI: 10.14036/j.cnki.cn11-4513.2025.04.008
Olympicism and Sports Culture Communication

Analysis of Research Hotspots and Evolutionary Pathways of the Olympic Movement

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Abstract

To explore the research hotspots and development trends of the Olympic Movement, this study takes the relevant literatures indexed in two Chinese and foreign literature databases as the analytical basis, and uses CiteSpace software to conduct a visual analysis of the above-mentioned Chinese and English documents. The research findings are as follows: 1) In terms of publication quantity, English literatures on the Olympic Movement appeared earlier and have shown a relatively steady increase in publication volume, while Chinese literatures emerged later, with the quantity surging to a peak in 2008 and gradually declining after the event; 2) In terms of publication countries and institutions, the United States has the largest number of publications, followed by China; scholars from European countries dominate the theoretical research of the Olympic Movement, while scholars from Middle Eastern countries have emerged prominently; 3) In terms of research hotspots and evolutionary pathways, Chinese literatures focus on the connotations and its related concepts of the Olympic Movement, the Olympic Games, Olympic culture, and the Olympic spirit, emphasizing the connection between Olympic culture and China’s local sports practice, English literatures explore multi-faceted perspectives from a global view, with research themes reflecting interdisciplinary and multi-directional characteristics; 4)In terms of research frontiers, Chinese and English literatures share commonalities but also have different focuses. Finally, based on the national strategy of constructing a sports power nation, suggestions are proposed from the perspectives of theoretical construction, academic practice, and policy transformation.

Key words

olympic movement / CiteSpace / knowledge graph / research hotspots / evolutionary path / bibliometric analysis

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GAO Lulu , LIU Tianxing , LASZLO Vajda. Analysis of Research Hotspots and Evolutionary Pathways of the Olympic Movement[J]. Journal of Capital University of Physical Education and Sports. 2025, 37(4): 433-444 https://doi.org/10.14036/j.cnki.cn11-4513.2025.04.008

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Develop and evaluate a natural language processing application for classifying chief complaints into syndromic categories for syndromic surveillance.Much of the input data for artificial intelligence applications in the medical field are free-text patient medical records, including dictated medical reports and triage chief complaints. To be useful for automated systems, the free-text must be translated into encoded form.We implemented a biosurveillance detection system from Pennsylvania to monitor the 2002 Winter Olympic Games. Because input data was in free-text format, we used a natural language processing text classifier to automatically classify free-text triage chief complaints into syndromic categories used by the biosurveillance system. The classifier was trained on 4700 chief complaints from Pennsylvania. We evaluated the ability of the classifier to classify free-text chief complaints into syndromic categories with a test set of 800 chief complaints from Utah.The classifier produced the following areas under the ROC curve: Constitutional = 0.95; Gastrointestinal = 0.97; Hemorrhagic = 0.99; Neurological = 0.96; Rash = 1.0; Respiratory = 0.99; Other = 0.96. Using information stored in the system's semantic model, we extracted from the Respiratory classifications lower respiratory complaints and lower respiratory complaints with fever with a precision of 0.97 and 0.96, respectively.Results suggest that a trainable natural language processing text classifier can accurately extract data from free-text chief complaints for biosurveillance.
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