Sports College Students and Entrepreneurship: An Investigation into Entrepreneurship Tendencies
Bibliographic record
Abstract
The purpose of this research is to examine the relationship between the entrepreneurship tendencies and self-efficacy of sports college students and whether there is a difference in terms of some demographic variables. The research is a quantitative study based on a relational survey model. The sample of the study consists of 495 participants who continue their education as active students in sports colleges affiliated to the Directorates of National Education in Konya and Kahramanmaraş in Turkey. The data of the research were analysed using a statistical software program. The arithmetic mean and standard deviation values were determined for the data analysis, and the t-test and One-Way Analysis of Variance (ANOVA) tests were used to determine the differentiation of the scores obtained for the variables. In addition, a correlation analysis was used to determine the relationship between variables, and a regression analysis was used to test whether the independent variables predict the dependent variable or not. As a result of the research, it was observed that the entrepreneurship and self-efficacy mean scores of the participants were at a moderate level and that the scores of the participants regarding entrepreneurship and self-efficacy levels differed significantly in terms of the variables of the province, the gender, and the grade level. It was also concluded that as the entrepreneurship perception of the students of sports college increases, their self-efficacy increases and the sub-dimensions “self-confidence, innovativeness, control focus, risk-taking and desire to accomplish” are significant predictors of students’ general self-efficacy while the dimension of “making difference and determination” is not a significant predictor of their general self-efficacy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".