Examination of the Effect of Badminton Education on Physical and Selected Performance Characteristics
Bibliographic record
Abstract
This study was conducted on a total of 16 female students studying at the 1st grade of Bingöl University, School of Physical Education and Sports. Students participated in the study voluntarily and divided into 8 experimental groups (EG) and 8 control groups (CG). In the study, height, body weight, dominant hand grip strength, non-dominant hand grip strength, back strength, leg strength, 30-meter speed running, flexibility measurement, vertical jump, long jump tests were applied to the experimental and control groups as pre-test and post-test. Statistical analysis of the study was performed using SPSS 22.0 program. The analysis of the data in-group and intergroup measurements were performed by MANOVA and p < 0.05 was taken as statistical significance level. While there was no statistically significant difference between the experimental group and the control group in terms of pre-test measurement results, there was a statistically significant difference in body fat percentage, body weight, body mass index value in favor of EG in physical measurements after 8-week training. Also, in terms of performance characteristics, according to the post-test measurement results, the differences between experimental and control group dominant hand strength and non-dominant hand strength, 30 m speed average was significantin favor of EG, and again the difference between long jump and vertical jump tests was statistically significant in favor of EG in final test measurement results. As a result, it can be said that 8-week basic badminton training has positive effects on female students both physically and physiologically.
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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 | 0.000 | 0.001 |
| 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) | 0.003 | 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".