Bibliographic record
Abstract
1. Penggunaan buaian. 2. Lagu pengantar tidur. 3. Menyusui. 4. Membeli ASI. 5. Penggunaan ibu susu. 6. Menyapih anak. 7. Sang ibu turun dari rumah. 8. Ibu dan anak turun di Tawailia dan Napu. 9. Ibu dan anak di Besoa. 10. Ibu dan anak di Bada'. 11. Buriro (Bada' Buriro'). 12. Anak dibawa turun ke bawah (kelompok Kaili dan Sigi). 13. Ibu dan anak dalam kelompok Pakawa. 14. Pada kelompok Kulawi. 15. Pada kelompok Koro. 16. Saat anak menyeberangi sungai. 17. Berkunjung bersama bayi. 18. Hadiah dari ayah kepada mertuanya untuk anak pertamanya. 19. Motinuwui untuk anak. 20. Meawoloi di Bada' dan Besoa. 21. Rabonto'oni di Rampi. 22. Mopahiwu di kalangan suku Koro dan Kulawi. 23. Pengorbanan anjing untuk bayi yang baru lahir, mebau. 24. Bayi menangis terus menerus. 25. Arti bintik-bintik pada kulit anak. 26. Makna ciri-ciri tubuh lainnya. 27. Pemberian nama. 28. Mengganti nama. 29. Mengadopsi anak. 30. Usia anak. 31. Tumbuh gigi. 32. Pergantian gigi. 33. Rambut dan kuku anak dipotong untuk pertama kalinya. 34. Apa yang terjadi pada rambut (kuku) yang dipotong? 35. Sunat dan asal usulnya. 36. Sunat pertama atau sunat pura-pura pada beberapa kelompok. 37. Perayaan untuk anak perempuan. 38. Sunat di antara suku-suku pegunungan. 39. Operator dan upahnya. 40. Operasi. 41. Pengaruh wanita terhadap sunat. 42. Setelah sunat. 43. Tujuan sunat. 44. Menusuk daun telinga. 45. Membakar lengan. 46. Mutilasi gigi. 47. Operasi dan operatornya. 48. Setelah operasi. Gigi dihitamkan. 49. Asal usul pemotongan gigi. 50. Sebab-sebab pemotongan gigi. 51. Permainan anak- anak.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.761 | 0.704 |
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".