‘Journal of the International Society of Biomechanics in Sports(ISBS)’의 연구동향 분석
Bibliographic record
Abstract
The purpose of this paper is to provide academic information on research methods and tasks which researchers in sport biomechanics should carry out so as to improve the quality of research on sport biomechanics. For this purpose, 242 articles published on ISBS from 2002 to 2012 were reviewed with a focus on topics, authors, subjects of research, and research methods in terms of an analysis of research trend. The summarized results are as follows: First, The result of the analysis of research topics is that Applied Research-② is the most frequently showed, followed by Applied Research-③, Basic Research-③, Applied Research-①, Basic Research-②, Applied Research-④, Basic Research-①. Second, The result of the analysis of the number of research participates is that 30.6% three, 26.0% two, 21.9% four, 9.5% five, 7.4% single and 4.5% six or more were indicated. For related to concentration of the authors focusing related field 31.4% physical sciences, 25.2% sport biomechanics, 13.2% physical education, 7.4% sports associations/ institute, 2.9% physics, 2.1% engineering, 1.2% biology, 1.2% neuroscience were indicated. Also, The nationalities of the leading authors, 27.7% USA, 19.0 Australia, 14.0 UK, 7.9% Japan, 5.4% Canada, 5.4% New Zealand were indicated. Third, The result of the analysis of research subjects is that 61.3% males, 29.5% both sexes and 9.2% females were indicated. The most frequently selected sports forms were elite sport. Most chosen games were athletics, gymnastics, soccer, cricket, tennis, baseball, swimming, and rowing, sequentially. Fourth, The result of the analysis of research method is that 54.5% cases Image analysis, 15.3% cases force platform, 11.5% cases measurement, 10.1% cases qualitative research, 3.1% cases EMG.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.011 |
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".