Determination of Attitude Levels of Teachers Working in School for Disabled Students Toward the Sportive Activities of Mentally Disabled Individuals (Muş Province Example)
Bibliographic record
Abstract
The main aim of this study is to determine what factors affect the attitudes of teachers working in the special education school toward their occupations. In this study, effect of teachers was investigated in terms of gender, teaching domains, sports branches and age. It was carried out with the participations of totally 40 special education teachers working in the body of Mus Province National Education in 2018-2019 education-training year. The scale of attitude toward the sportive activities of mentally disabled individuals was developed by İlhan, Esentürk, and Yarımkaya (2016) (ZEBSEYTÖ). Validity and Reliability working sample was used. SPSS 23.0 package program was used in the analysis of data obtained and finding of the calculated values. It was determined that data do not show normal distribution by performing test of normality. For this reason, Mann Whitney-U test was used for the pairwise independent groups, error margin was taken 0.05 in this study. Once the attitude scale sub-dimensions were examined, no statistically significant difference was found between the genders. When the sub-dimensions of attitude scale were examined, no statistically significant difference was found between whether the participants engage in a sports branch or not. Significant difference was not seen in the participants in terms of doing sports and age.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".