Individual Innovation: A Research on Sports Manager Candidates
Bibliographic record
Abstract
In this study, it was aimed to determine the level of individual innovation of sports manager candidates. The research is designed according to the relational survey model from the survey models. The sample of study consists of 249 sport manager candidates studying in Gazi University and Ankara University Faculty of Sport Sciences Sports Management Department in 2017–2018 spring semester. In the study, in order to collect data, “the Personal Innovation Scale (PIS)”, which was developed by Hurt, Joseph and Cook (1977) and validated by Kilicer and Odabasi (2010) in accordance with the Turkish literature, was used. In the analysis of the data obtained from the study group, the normality test (Kolmogrow-Smirnow and Skewness-Kurtosis), Independent t-test and One Way Anova tests were used. According to the results of the research, it can be stated that the participants’ levels of individual innovation is at the intermediate level and they are included in the “interrogator” category. The Scores of Participants from the Individual Innovation Level Scale do not include any significant difference in the sub-dimensions of idea leadership, openness to experience, risk taking, resistance to change according to the variables of gender and being a licensed athlete; whereas a significant difference was found in favor of 1st and 2nd grades in terms of openness to experience sub-dimension according to the class level variable, and in favor of participants taking part in sports organizations in terms of the idea leadership sub-dimension according to the variable of taking part in sports organizations.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".