MEDIA WARAGA SEBAGAI UPAYA MENINGKATKAN KEMAMPUAN SISWA SEKOLAH DASAR DALAM MENGIDENTIFIKASI KERAGAMAN BUDAYA
Bibliographic record
Abstract
This research aims to describe the enhancement of elementary school students ' ability to identify cultural diversity, as well as describe the improvement of basic skills teaching teachers through the application of Waraga media.The research method used is a class action research conducted as many as 2 cycles. Each cycle is implemented during 2 meetings. Cycles consist of planning, acting, observing, and reflecting. The subject of the study was 28 graders of SD Negeri 2 Pecangaan Wetan in Lesson 2019/2020. The technique of data collection during the implementation of class action research using test techniques in the form of evaluation test to know the improvement of students ' ability to identify cultural diversity, and nontest technique in the form of observation of teacher teaching basic skills using observation sheet consisting of 8 observed aspects. Data analysis used is quantitative and qualitative data analysis. The results showed that with the use of the media waraga in learning occurs increase the ability of students in identifying cultural diversity in elementary schools. Proven from the increasing percentage of the classical dictancy. Cycle I gained a percentage of 68% and cycle II gained 86%. The results also showed improved teacher teaching basic skills, proven from observations that gained a score percentage of 77.3% on the I cycle and increased to 85% in cycle II.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".