Bibliographic record
Abstract
This study was conducted to determine the role of job involvement and personality traits in teachers' job efficiency using a correlation method.The statistical population included all teachers of Tehran schools in the academic year.A sample size of 200 individuals were selected by random sampling method according to the Morgan table.Data were collected using the NEO personality characteristics scale, Konongo's job involvement scale (1982), and job efficiency scale.Data were analyzed by Pearson correlation and multivariate regression.According to the results, a value of 0.136 means that 13% of the variance of job efficiency variable is explained by six components of psychological neuroticism, extraversion, openness to experience, appropriateness, conscientiousness, and job involvement.In addition, the calculated F ratio (05.081) was significant at a confidence level of at least 99%.Therefore, it can be concluded that there is a significant correlation between the examined variables and job efficiency variable.Considering the obtained coefficients, it can be concluded that the variables of conscientiousness, appropriateness, and extraversion were negatively and significantly correlated with job efficiency of teachers (p < 0.01).There were positive and significant correlations between the variables of job involvement, neuroticism, and openness to experience with job efficiency in teachers.Given the regression coefficients and its linear equation, the two variables of conscientiousness and job involvement can be included in the regression equation at an acceptable level with a strong predictive power.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.947 | 0.952 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".