INTERVENSI DEWA DALAM MENGATUR KEHIDUPAN MANUSIA DALAM LEGENDA CHANG E
Bibliographic record
Abstract
Legenda Chang E mengisahkan kehidupan Hou Yi dan istirnya yang kemudian berubah menjadi Dewi Bulan. Penelitian ini mendeskripsikan intervensi dewa dalam mengatur kehidupan manusia dalam struktur legenda Chang E. Selain itu, penelitian ini mendeskripsikan pula konteks penuturan, proses penciptaan, proses pewarisan, dan fungsi legenda Chang E. Teknik pengumpulan data dilakukan dengan studi pustaka, wawancara, dan observasi. Setelah itu, dilakukan transkripsi, transliterasi, dan analisis legenda Chang E menggunakan strukturalisme Todorov. Dalam penelitian ini ditemukan bahwa intervensi dewa diperlihatkan dalam struktur legenda yaitu pada proses istri Hou Yi berubah menjadi Dewi Bulan saat obat keabadian yang ditemukan Hou Yi dari pertapaannya. Obat keabadian tersebut dibuat oleh Kelinci Giok, teman Dewi Bulan. Dalam konteks penuturan, legenda Chang E dituturkan agar orang-orang dapat mengetahui cerita mengenai Dewi Bulan, yang berkaitan dengan Festival Bulan Purnama. Proses penciptaan Legenda Chang E tergolong sederhana karena diciptakan secara terstruktur. Legenda Chang E memiliki enam fungsi, yakni sebagai sistem proyeksi, pengesah kebudayaan, sebagai alat pendidikan anak, memberikan jalan yang dibenarkan oleh masyarakat agar seseorang merasa lebih superior, memprotes ketidakadilan di masyarakat, dan sebagai hiburan. Sampai saat ini, legenda Chang E masih terus diartikulasikan dikalangan masyarakat keturunan Tionghoa yang tinggal di Bandung Raya.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.006 |
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".