Bibliographic record
Abstract
This research examines the feasibility of training nonverbal communication by Shiraz teachers teaching Persian alphabet and the main problem of this study is that how much education authorities such as teachers have direct relationship with students, how much they have knowledge on no-verbal communication and how much they use it.Method of this study was content analysis and its population included first-grade elementary teachers of Shiraz that 40 of them were selected purposefully by elementary education experts.In this study, the required information was collected by trained people and under the supervision of a researcher by attending in the classroom in uncontrolled observation way.Then, they were analyzed using SPSS software.In this study, nonverbal behavior of teachers in the classroom, such as the type of role, the type of emotional movement, distance from student, facial expression, head and hands movements, looking, funny movements, blinking, eye state, type of hands and feet movement, physical state, the state of the fingers, eye contact effectiveness, expression of emotions, and intervention of teachers in the student speech were examined and analyzed.This study indicates that the first-grade elementary teachers of Shiraz have less skill in the use of nonverbal behavior, and the role of teacher is more formal and in expressing their emotions, they use more combination of face and hands and the distance between teacher and student is more public than social type, and face expression of teachers shows interest and passion for education and most of teachers have no reliance state in teaching.In addition, head of teachers is on the state toward students and use of eye contact is high and emotional state of teachers is learning type, tone of teachers is interrogative, and clothing of teachers is more formal.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.890 | 0.882 |
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".