PENGGUNAAN CONTEXTUAL LEARNING AND TEACHING DALAM PELATIHAN BAHASA INGGRIS BAGI PASSENGER SERVICE OFFICER KERETA CEPAT JAKARTA BANDUNG
Bibliographic record
Abstract
Dalam rangka Perencanaan Kereta Cepat Jakarta-Bandung (KCJB) yang akan dioperasikan pada akhir tahun 2023, maka perlu adanya layanan yang dipersiapkan dalam melayani calon penumpang adalah Passenger Service Officer on Train (PSOT). Untuk itu kemampuan menggunakan Bahasa inggris bagi PSOT penting untuk menunjang komunikasi penumpang mancanegara. Tujuan dari kegiatan ini adalah memberikan pelatihan Bahasa inggris bagi Passanger Service Officer dengan metode Contextual Learning and Teaching (CTL). Evaluasi kegiatan ini mengunakan google form dengan skala likert. Hasil dari evaluasi kegiatan ini meunjukkan bahwa kegiatan pelatihan memberikan manfaat bagi pekerjaan PSOT dengan hasil evaluasi 75,12% menyatakan sangat baik untuk penampilan dan kemampuan pemateri, dan 73,55% memilih kriteria excellent pada kriteria materi.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".