Bibliographic record
Abstract
The present study aims at the survey of the relationship between emotional intelligence with academic self-efficacy in high school students from Orumia in curriculum year of 2015-2016.For the same purpose, 234 individuals out of 613 students were selected based on Morgan's table as the study sample volume, including 117 individuals from third grade and 117 individuals from the fourth grade all majoring in math, humanities and natural sciences.The sampling method of choice has been stratified sampling.To gather the required information, Bar-on's emotional intelligence questionnaire and Morgan-Jenkins's selfefficacy questionnaire were applied.To analyze the data, descriptive statistic methods such as mean, median, exponent and standard deviation were used and univariate and multivariate regression tests were simultaneously implemented in the inferential statistics part.The results indicated that there is a significant relationship between the emotional intelligence and academic self-efficacy.Furthermore, the regression results demonstrated that emotional intelligence account for 81%, respectively, of the students' academic self-efficacy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 1.000 | 0.999 |
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; both teacher heads 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".