Investigation of the Perceptions of Students Who Participate in Sports Organizations in Universities Regarding the Term “Sports”
Bibliographic record
Abstract
The main aim of this study is to investigate the perceptions of students who participate in sports organizations in universities regarding the term “sports”. With this main aim, the perception of the term was explained by metaphors. The study group includes 108 female and 112 male volunteer students, who studied in various departments of Adıyaman University during the 2017-2018 education period. For the data collection tools in the study, a short personal information form and the form that asks to complete the sentence, “Sports are like … Because …” In the data analysis, with the SPSS 24.00 package software, individual variables were classified and four steps were followed in the analysis of the metaphors. These steps are naming the metaphors, elimination and refinement, compilation and categorization, and the findings were obtained by validity and reliability analyses. As a result of the research, it was determined that 88.7% of the students who participated in sports organizations in universities developed positive metaphors while 11.3 developed negative metaphors. Additionally, it was concluded that these metaphors created significant differences according to students’ genders, and the departments and programs of study. As a result of the study, it can be concluded that university students participating in sports activities voluntarily have a perception of “sports” as improving health, regarding as a profession and gaining social prestige and this result affects their future life positively.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".