Social Capital’s Effect on Physical Education and Teachers’ Job Satisfaction
Bibliographic record
Abstract
This study tested the impact of physical education (PE) teachers’ social capital on job satisfaction and explained levels of social capital for job satisfaction. Study participants were 210 PE teachers. The research methodology used the correlational survey model, and the instruments administered were the Social Capital Scale,and the Minnesota Job Satisfaction Scale. For conducting scales’ confirmatory factor analyses and structural equation modeling, SPSS 23.0 and AMOS 17.0 software were used. The model’s goodness fit index was: RMSEA = 0.081; SRMR = 0.082; CMIN\DF = 2.523; GFI = 0.922; CFI = 0.923; AGFI = 0.843; NFI = 0.913; Chi squared = 2832.001; df = 976 and p = 0.000. According to these results, the model fit index reached an acceptable and desired level. The effect of social capital on job satisfaction and the rate of explaining job satisfaction were tested. In relation to the study’s first hypothesis, it was revealed that PE teachers’ social capital level and job satisfaction were positively and significantly affected. In regard to the second hypothesis, there was a significant relationship between social capital levels and PE teachers’ job satisfaction. The study’s most significant finding was that social capital significantly predicted PE teachers’ job satisfaction.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".