Bibliographic record
Abstract
1. Perkenalan. 2. Dukun pertama. 3. Nama perdukunan dan dukun. 4. Perdukunan di Bada'. 5. Siapa yang menjadi dukun. 6. Magang. 7. Festival penahbisan mobantu di Bada'. 8. Perayaan pentahbisan moliwa di Bada'. 9. Dukun laki-laki. 10. Pakaian dan perhiasan. 11. Jimat. 12. Dukun dalamkehidupan sehari-hari. Larangan makanan. 13. Pemakaman dukun. 14. Kerasukan dukun. 15. Bagaimana Roh Mewujud. 16. Bahasa roh yang mewujud. 17. Para pembantu para dukun. 18. Gaji dukun. 19. Mengembalikan semangat hidup yang hilang. Mobilia di Napu. 20. Mobalia di Besoa. 21. Mobalia di bagian lain daerah pegunung-an. Di Bada'. 22. Mobalia di Rampi'. 23. Mobalia diantara kelompok Koro. 24. Mobalia di kalangan kelompok Kulawi. 25. Perayaan mobalia khusus untuk penyembuhan orang sakit di Napu. Molelingi. 26. Mobatanda. 27. Medopi. 28. Motowugi. 29. Momandoro. 30. Puasa. 31. Mobalia di dataran rendah. 32. Mobalia tampilangi. 33. Mobalia totali. 34. Mobalia ompungi. 35. Mobalia topeule. 36. Mobalia jinja. 37. Mobalia bugi. 38. Bayasa. Mobalia Bone. 39. Festival dukun untuk meningkatkan kesehatan dan kesejahteraan masyarakat. 40. Nokeso di dataran rendah. 41. Kehidupan di dalam lagu. 42. Meja persembahan. 43. Berjalan ke air. 44. Mendandani anak-anak. 45. Turun ke tanah.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.425 | 0.329 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".