Examination of Body Areas Satisfaction Levels and Gender Roles of Female Wrestlers
Bibliographic record
Abstract
The aim of this study is to determine the relationship between the Body Areas Satisfaction Levels and Gender Roles of female wrestlers. The sample of the study is constituted by 39 female wrestlers in Turkey Olympic Preparation Center in Edirne in 2017. As the data collection tool in the research; to determine the Body Areas Satisfaction Levels “Multidimensional Body-Self Relations Scale”, to determine the gender roles “BEM Gender Role Inventory” was used. In the evaluation of the data obtained, SPSS 20.0 statistics program was used. In the analysis of data, descriptive statistics; in paired comparisons, t-test; and in multiple comparisons, ANOVA test was used. As a result of statistical analysis made, it was observed that, of the female wrestlers; 15.4% had masculine, 35.9% had feminine, 17.9% had androgynous and 30.8% had unclear gender role behaviours. Significant differences were found between the score of femininity characteristics and score of masculinity characteristics and score of social acceptability characteristics (p<0.05). According to body mass index (BMI), regarding the clauses on satisfaction with body areas, there is significant difference; between the normal weight and the over weight as per the clause “I am satisfied with my lower body”; between the overweight and the thin and the normal weight as per the clause “I am satisfied with my central body”; between the weak athletes and normal weight athletes as per the clause “I am satisfied with muscle structure”; between the normal weight and over weight athletes as per the clause “I am satisfied with my weight” (P<0.05). There was no significant difference according to BMI variable in the clauses “I’m satisfied with my face”, “I’m satisfied with my hair”, “I’m satisfied with my upper body”, “I’m satisfied with my height” and “I’m satisfied with my overall appearance”.
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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 | 0.001 | 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".