Describing the Competence Perception Levels of Physical Education and Sports Teachers in Integrative Practices: Kirşehir Province Example
Bibliographic record
Abstract
The objective of this research study is to describe the competence perception levels of physical education and sports teachers in integrative practices. The participants of the study were the physical education and sports teachers actively working in Kırşehir province who participated on a voluntary basis. In this research, the survey model was used. In the data collection process of the study, “Personal Information Form” and “Teacher Adequacy Scale in Inclusive Practices” were used. For testing the research data, the significance level was accepted as 0.05. In the analysis of the data, the items of the sub-problem were grouped, and independent samples t-test and one-way analysis of variance (ANOVA), were used in the scale together with descriptive statistics such as frequency (f), percentage (%), weighted average (X) and standard deviation (SD), and Mann Whitney U and Kruskall Wallis techniques were used for the sub-dimensions. The results of the research demonstrated that physical education and sports teachers have a high level of competence perception in inclusive practices. It was also determined that there was statistically no significant difference among the competence perception levels of teachers in inclusive practices concerning gender, professional experience, working location, and educational status.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".