Non-verbal expressions in the students' didactical outputs at the Charles University, the Faculty od Physical Education and Sport in the subject Theory and basics of swimming didactics
Bibliographic record
Abstract
Title: Non-verbal expressions in the students' didactical outputs at the Charles University, the Faculty od Physical Education and Sport in the subject Theory and basics of swimming didactics Purpose: The aim of this diploma thesis is to consider the degree of selected aspects of the non-verbal expressions in the studens' didactical outputs at the Charles University, the Faculty of Physical Education and Sport and to evaluate non-verbal differences between males and famales and between students with more focus on swimming and those who only participated in swimming lesson obligatory. Methods: Observation and video analyzing methods were used for the reserch of the selected aspects. The analysis of the video recording results is presented in graphs. Results: The video analysis method seems to be appropriate for examining the students' didactical outputs. The frequencies of the selected non-verbal acpects of the individuals in the research are intraindividually different. When comparing the students' non-verbal expressions defferences were found in quarter of the observed cases. The differences among students with a higher focus on swimming and those who only compledes obligatory swimming lessons appeared in all cases. They were positive for the students with a higher focus on swimming, but in the...
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".