Bibliographic record
Abstract
Abstract Although soccer is a popular sport worldwide, little work has been done to satisfy the increasing demand for quantitative research on female players. As a result, training programmes for female players are often taken directly from their male counterparts, without appropriate adaptations. In this study, I examine the influence of gender and experience on the maximal instep soccer kick among male and female college students, with equal numbers of novice and skilled players. The data collection equipment consisted of a synchronized system with VICON™ 3D motion capture (nine high‐speed cameras, 120 Hz) and NORAXON wireless electromyography. Results showed that trained male and female players have different techniques. After a powerful kick, males naturally follow through with a jump to dissipate residual leg momentum, whereas females avoid this airborne phase; instead, they counteract the momentum with upper‐body flexions. Skilled male players displayed a more powerful quasi whip‐like movement of the kicking leg and more explosive muscle work patterns (higher maximum and faster increase rate of muscle tension) than skilled females. During training, practitioners should pay special attention to repetitive injuries in small muscles like the adductor magnus. The differences observed may be important for the development of training programmes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".