Peningkatan Kedisiplinan Melalui Media Stik Ice Cream Pada Siswa Kelompok B di TK Al Hidayah Jambewangi 01
Bibliographic record
Abstract
Character education is very important to instill in children from an early age. therefore, schools which are places that contribute greatly to the formation of children's attitudes, are obliged to include elements of character education in their students. One of these characters is the character of discipline. Children are not directly involved in learning activities and everyday problem solving. Yet this is very important for the development of children. Therefore, it is necessary to find a solution to this problem by using ice cream stick learning media. The ice cream sticks will be carried out using a reward and punishment system. In addition, giving punishment to children is illustrated by bad things, such as scolding children and punishing children, but the purpose of the teacher or parents giving punishment is so that children become disciplined in the future and do not repeat the mistake he had made. Based on the results of data analysis in the pre-study, a percentage of 44% was obtained, while in cycle I, a percentage of 60% was obtained. In cycle II, the percentage reached 82%, with the acquisition of these data there was a significant increase in children's disciplinary behavior in pre-research to cycle I and from cycle I to cycle II. Based on these data, this research can be said to be successful because in cycle II there was an increase, and it was included in the good category
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.191 | 0.040 |
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".