The IJSPP Twitter Account: Our Secondary Step to Narrow the Gap Between Sport Science and Sport Practice
Bibliographic record
Abstract
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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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.261 | 0.192 |
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".