Bibliographic record
Abstract
1. Tiga kelompok roh. 2. Lai dan Ndara. 3. Pue-di-songi. Ngkai-mantande-songka. 4. Pue-mpalaburu. 5. Sang Pencipta dan Ciptaan. 6. Kisah penciptaan lainnya. Banjir. 7. Orang ditakdirkan untuk tidak mati. 8. Takdir. 9. Surga. 10. Burung sebagai utusan para dewa. Pedang. 11. Meramal dengan tali peramal. 12. Meramal dengan biji jagung. 13. Pemisahan objek ramalan. 14. Meramal dengan mengukur. 15. Hubungan antara manusia dan dewa. Gagasan tentang dosa. 16. Penebusan dosa. 17. Roh Wurake. 18. Nama Wurake. 19. Desa Wurake. 20. Rumah Wurake. 21. Makanan roh Wurake. 22. Para Kepala Suku Wurake. 23. Peperangan oleh Wurake. 24. Para budak Wurake. 25. Dewa ladang yang bersifat umum. 26. Roh ladang pribadi. 27. Kelompok roh dan komunitas ladang. 28. Hukuman roh ladang. 29. Dewi Padi (Lise). 30. Dewi Padi sebagai burung. 31. Roh-roh yang merugikan orang-orang di pertanian mereka. 32. Roh-roh pohon. 33. Roh waringin. 34. Kontak roh-roh pohon dengan manusia. 35. Roh-roh hutan khusus. 36. Roh air. 37. 37. Roh Imbu. 38. Roh tanah. 39. Roh batu. Watu-mora'a. Tagoralangi. 40. Makna yang terlekat pada beberapa batu. 41. Membatu. 42. Roh memberi orang kekuatan gaib. Mobaratapa. 43. Penghormatan arwah leluhur dalam rumah tangga. 44. Penghormatan leluhur dalam suku. Anitu .
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.509 | 0.268 |
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".