Olympic education as an object of scientific analysis based on international scientometric databases Scopus and Web of Science
Notice bibliographique
Résumé
Background and Study Aim. Studying the history of the Olympic movement, one can be convinced that the ideals and values that underlie the Olympic Games of Ancient Greece are relevant today. A mandatory requirement for any country hosting the Olympic Games is the introduction of Olympic education. There is a scientific interest in a comprehensive and objective consideration and study of Olympic education, taking into account all the strengths and weaknesses, achievements and problems, weaknesses and shortcomings, which determines the relevance of this study. The purpose of the article is to systematize modern scientific ideas about Olympic education based on an analytical analysis of the resources of the international scientometric databases Scopus and Web of Science. Material and Methods. to systematize modern scientific ideas about Olympic education, 4,435 sources in the scientometric databases Scopus and Web of Science were analyzed. Bibliometric methods of information processing in the context of Olympic education were used. For this, the VOSviewer program, version 1.6.18, was used: the method of keyword analysis and direct citation analysis with the construction of bibliometric maps, visualization of cluster density, weights – citations. Results. As of 21.02.2024, 4,435 sources were found in the scientometric databases Scopus and Web of Science for the keyword "olympic education". The most popular categories of publications by quantitative indicator, authors, journals, countries that have the largest number of publications on the studied problem of Olympic education were determined. The largest cluster of keywords in the scientometric database Scopus is "education", and in Web of Science - the cluster "Olympic games". The constructed bibliometric maps made it possible to systematize modern scientific ideas about Olympic education and determine the most popular areas of research in the study of the problem under study: features of Olympic education programs in individual countries of the world; Olympic education of schoolchildren and students; educational potential of Olympic education; Olympic education management and marketing strategies; legacy of Olympic education. Conclusions. A fairly large number of works devoted to Olympic education have been identified. The largest number of works on scientometric bases relate to the following research areas: Sport Sciences, Social Sciences, Health Professions, Arts and Humanities, Business, Management and Accounting, Education Educational Research and other subject area. Most of the authors represent countries such as the USA, England, China, Canada, Spain, Australia, Germany, Portugal, Brazil and Japan. The conducted analysis allowed us to provide a comprehensive analysis of the problem and identify a number of unresolved scientific issues in Olympic education: search for new approaches to the development, implementation and evaluation of Olympic education programs; development of effective strategies for the implementation of Olympic education; search for innovations and technologies in the field of Olympic education; study of modern media tools for the development of the Olympic movement as a whole, etc.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,116 | 0,139 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,009 | 0,003 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».