The IJSPP Twitter Account: Our Secondary Step to Narrow the Gap Between Sport Science and Sport Practice
Notice bibliographique
Résumé
The IJSPP Twitter Account: Our Secondary Step to Narrow the Gap Between Sport Science and Sport Practice aaaOne of the strengths of the International Journal of Sports Physiology and Performance (IJSPP) is the section of Practical Applications in each published article.This section is intended to address our mission to "promote the publication of research in sport physiology and related disciplines that has direct practical application to enhancing sport performance, preventing decrements in performance, or enhancing recovery of athletes."With the Practical Applications section, we try to narrow the often-mentioned gap between sport science and sport practice.1-3 By clear identification of authentic practical advice for athletes and practitioners, 2 we are attempting to "sow the fields for later harvest."However, just sowing the fields is not enough.A next step in narrowing the gap between science and practice is to actively deliver our message to the end users-the coaches-at the field, court, pool, or track.One obvious vehicle for this strategy is social media.It is clear that almost everybody is nowadays "connected" and that the amount of information spread on a variety of social media platforms is enormous.This challenges us to think of how IJSPP's message can stand out from the masses.That is not a complicated process.Just be the same as we always have been and, as is the case for the Practical Applications, deliver authentic practical advice to our readership.To accomplish this, IJSPP has appointed a Social Media Editor, Teun van Erp, who will lead us, together with Associate Editors Rob Lamberts and Stephen Cheung and Editorial Assistant Dionne Noordhof, in this task.Teun has experience as a sport scientist and has also been involved, for more than 10 years, as a leading scientist with one of the major Union Cycliste Internationale World-Tour cycling teams.As such, he has a very keen eye on the application of science in the field and is very well equipped to lead this task.For many scientists and also scientific journals, Twitter is the most popular platform to create social awareness about recently published papers and issues.Just like word of mouth is the best form of advertising, oftentimes the best notice for a paper comes from fellow scientists talking about the work.Posting can take many forms beyond a simple summary and link crammed into 280 characters.For some authors, it is also a creative pursuit, ranging from including key figures to infographics or image files summarizing the paper.Posting about papers is more than self-promotion.Instead, it is a venue for scientific communication to the end users.As part of this activity, it is important for journals to have a high number of Twitter followers, as this increases the social impact of a journal's tweets.In return, the tweets are an easy way for scientists, coaches, and other people interested in sport science research to keep track of recently accepted or published papers.The number of Twitter followers of the top-15 scientific journals ranges from 837 to 76,300.Interestingly, there is a strong correlation (r = .81)between a journal's number of Twitter followers and its impact factor (Figure 1).This highlights the fact that a scientific article in a journal not only needs to be of high quality but also needs social awareness in order to optimally share the body of knowledge with the end users.
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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 | 0,003 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,004 | 0,006 |
| Études des sciences et des technologies | 0,005 | 0,001 |
| Communication savante | 0,009 | 0,014 |
| Science ouverte | 0,001 | 0,009 |
| Intégrité de la recherche | 0,005 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,261 | 0,192 |
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 ».