PEMBELAJARAN BAHASA INGGRIS MENULIS NARRATIVE DI PADLET DAN BERBICARA DENGAN TALK SHOW
Bibliographic record
Abstract
Praktik terbaik dalam pembelajaran Bahasa Inggris dengan menulis dan berbicara ‘Narrative’ dengan talkshow bertujuan untuk menerapkan kemampuan peserta didik menulis cerita narrative dan berbicara dengan ‘Talkshow.‘ Metode ini menggunakan deskriptif kualitatif. Data yang diperoleh berdasarkan observasi, pelaksanaan pembelajaran dan kuestioner. Subjek penelitian ini peserta didik kelas XI SMA Negeri 1 Bantul. Berdasarkan praktik terbaik dalam pembelajaran tersebut yang dilakukan yaitu penjelasan materi ‘Narrative’ kegiatan berkelompok dalam menulis dan berbicara dengan talkshow. Berdasarkan hasil kuestioner dan praktik menulis cerita dan berbicara dengan talkshow peserta didik dapat menulis cerita , praktis menulis di Padlet, kreatif, menyenangkan, pengalaman, meningkat tata bahasa dan kosakata, punya percaya diri, memperbaiki kemampuan berbicara, bekerja sama dan saling bercerita dengan santai
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.063 | 0.011 |
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".