Notice bibliographique
Résumé
The evolution of sports science has seen the emergence of different forms of employment: practicing sports scientists in state-sponsored (primarily) Olympic sports, academic researchers with an interest in sports physiology and sports performance, self-employed private practitioners, and team-based support staff with a blend of coaching, sports science, and strength and conditioning duties.Like most jobs, the key issues are the type and interest of the work, level of remuneration, job security, and future growth prospects for the individual and discipline.The position of practicing sports scientists continues to emerge in selected sports.Many nations and their national sporting programs employ or contract sports scientists in the quest for international success.Historically, much of this work has been conducted in the individual sports, such as cycling, rowing, swimming, triathlon, and distance running, where physiological characteristics and capacities are important determinants of performance.The United States has a unique sports system, with only a limited number of sports scientists working directly with summer and winter Olympic sports.This is offset by a huge collegiate and professional sports system that offers unparalleled resources and opportunities for researchers, students, and practitioners.The challenge for other nations is to build sports science into their sporting system as a fundamental element.At times, sports science and sports physiology are considered luxuries and lower in the list of priorities than (essential) disciplines, such as strength and conditioning, sports medicine, and physical therapies.Some countries, such as Russia, Germany, and Canada, that were leaders in the emergence of sports science in the 1960's, 70's, and 80's have faced significant challenges as the field has matured internationally.Can countries like China, Japan, and India capitalize on their emerging economic strength and sporting prowess to take sports physiology and performance enhancement to another level?Academic research is another active of area of exercise and sports science research.The balance between these two subdisciplines has changed substantially in the last 5 years with a worldwide focus on physical activity, obesity, metabolic syndrome, and related lifestyle and medical issues.The funding for these biomedical areas is often orders of magnitude greater than that directed to sports physiology and performance.Only a few years ago, most postgraduate projects were focused on sports science and exercise/sports performance.Nowadays, most graduate student projects are in the area of physical activity.This trend has also changed the face of undergraduate programs.The previous emphasis of courses in exercise physiology and sport performance is being replaced by physical activity for special populations, exercise programs for the sedentary or obese, and other lifestyle issues.Universities are recruiting staff with expertise and skills in these areas, and, of course, with matching publication and funding track records.Internal university and department funding is directed toward physical activity, so those staff members and students
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 | 0,004 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,010 | 0,016 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,007 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,040 | 0,009 |
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 ».