The Relationship Between School Administrators’ Happiness Levels and Their Self-Efficacy Levels
Bibliographic record
Abstract
The aim of this present research is to specify the interrelation between the happiness and self-efficacy levels of the school administrators. This study is a descriptive survey model, and its population consists of the school principals and deputy principals in Amasya Province which are the subsidiaries of the Ministry of National Education. The Correlation coefficient was calculated and the methods of One-Way ANOVA, t-test, and Kruskal-Wallis H tests were used for the sub-problems. Once the findings of the research were analysed, a positive and mid-level significant interrelation was discovered between the happiness and the self-efficacy levels of the administrators about school administration. The results of the analysis suggest that happiness and self-efficacy levels of the school administrators according to their perceptions can be observed as “fine”. Furthermore, their perceptions about happiness and self-efficacy levels differ according to the length of service groups they belong to. This is evident from the finding that the group of 1-5 years of service has highest score of happiness level, and the experience groups of 6-10, 16-20, 21 and above, and 11-15 years follow them respectively. The highest score of self-efficacy level, at the same time, is owned by the ones who have 21 years of service and above, and the experience groups of 16-20, 6-10, 11-15, and 1-5 years follow them respectively. The self-efficacy levels also show significant difference regarding the variable of 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 | 0.001 | 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 | 0.001 |
| 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".