Studi Deskriptif Instrumen Kucapi Pakpak di Kabupaten Pakpak Bharat
Bibliographic record
Abstract
Tujuaan penelitian adalah: (1) Untuk mengetahui teknik yang digunakan bermain Kucapi Pakpak di Kabupaten Pakpak Bharat, (2) Untuk mengetahui estetika instrumen Kucapi Pakpak di Kabupaten Pakpak Bharat, (3)Untuk mengetahui peran permainan Kucapi Pakpak dalam melestarikan budaya dan tradisi masyarakat Kabupaten Pakpak Bharat. Teori yang digunakan dalam penelitian ini ialah teori teknik permainan,teknik dasar,teknik menganak-anaki, teknik membungabungai, teori teori estetika, serta teori pelestarian budaya. Sampel penelitian ini sebanyak 3 orang .yaitu pemain kucapi dan tokoh budaya di Kabupaten Pakpak Bharat. Metode penelitian yang digunakan adalah metode penelitian kualitatif dengan jenis deskriptif kualitatif. Teknik pengumpulan data yang digunakan ovservasi,wawancar, dokumentasi. Hasil penelitian menunjukkan : (1) Teknik pyang digunakan dalam penelitian ini ialah teknik dasar,teknik menganak-anaki, teknik membungabungai, (2) Estetika Instrumen kucapi Pakpak melambangkan susunan dan kedudukan dalam system kekerabatan Pakpak yang terdiri dari Kesukuten, Puang/ Kula-kula, Dengan Sebeltek, dan Berru. (3) peran permainan kucapi di masyarakat berperan dalam melestarikan budaya, karena kucapi ini memiliki fungsi sebagai ekpresi pengungkapan emosional, penghayatan estetis, upacara agama, perlambangan sosial politik.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".