Sports Education Institutions in Turkey and Their Managerial Effectiveness
Bibliographic record
Abstract
The aim of this study is to investigate the managerial effectiveness of administrators according to the opinions of theacademicians who work in sports sciences faculties in Turkey. In order to collect the opinions of the academicians,the “Managerial Effectiveness Evaluation (MEE) Scale” was adopted. The MEE scale consists of 5 subscales and 44matters. The gathered data was analyzed with parametric and non-parametric tests by using SPSS 22.0 packagesoftware. Additionally, in order to determine the level and course of the relationship between the dependent variables,“Pearson correlation analysis” was conducted. To better explain the quality of the determined relationship anddetermine the prediction between MEE scale and its subscales, “multiple regression” analysis was conducted.Considering the opinions of the academicians, significant differences were determined in the gender variable in the“Leadership” subscale (p< 0.05). In the correlation analysis, it was determined that the strongest relationship wasbetween the MEE scale and the “Planning and Decision-making” subscale (r= 0.980; p< 0.001) in a positive way andat very high level. With the regression analysis, four distinct model structures were built with 6 independentvariables (MEE scale and its subscales). As a result of these 4 model structures, it was determined that the subscalewith the strongest prediction of the MEE scale was the “Planning and Decision-making” subscale. Furthermore, itwas determined that this subscale predicted the 96% of the variance of MEE scale (R2=0.960).
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".