Bibliographic record
Abstract
This article studies Predictive Role of personality traits and creativity in 2015.Applying Cochran formula 1 , the study uses a predictor descriptive perfectionism as to male and female teachers at district 1, Zahedan, Iran in correlational method, statistical population of which consists of All male and female teachers at district 1, Zahedan, totally including 649, out of which 242 (128 female and 114 male) were selected randomly engaged in the general educational administrative.N.E.O .2(1985) personality characteristics, Creativity Torrens (1979), and Frost Multidimensional Perfectionism (1990) are the three questionnaires used in collecting data.In analyzing the data, descriptive and inferential statistics are applied.At the descriptive level, mean, standard deviation, frequency, percentage, and at inferential level, Pearson correlation coefficient and regression analysis have been utilized to determine the relationship among variables.As per the descriptive data, the respondents are mostly Female participants, at the age group of 28-38 married and indigenous.As revealed from the analytical results, the dimensions of personality characteristics (duteousness, agreeableness, openness and intro-extroversion) have significant negative relationships with perfectionism.At a level of 0.99 confidence, Creativity has significant positive relationships.The results indicate that among personality characteristics, neuroticism most evidently contributing to predicting Perfectionism and Creativity is not a predictive of perfectionism as to male and female teachers, at district 1, Zahedan.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.926 | 0.938 |
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".