PARTISIPASI LEMBAGA ADAT DAYAK AGABAG DALAM MELESTARIKAN BUDAYA ANGALANG DI WILAYAH KECAMATAN LUMBIS OGONG KABUPATEN NUNUKAN PROVINSI KALIMANTAN UTARA \nTAHUN 2017
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui partisipasi lembaga adat Dayak \nAgabag dalam melestarikan budaya angalang di wilayah kecamatan Lumbis \nOgong. Penelitian ini adalah penelitian deskriptif kualitatif. Penelitian dilakukan \ndi Kecamatan Lumbis Ogong, Kabupaten Nunukan. Subjek penelitian empat \norang yaitu Ketua Umum Dewan Adat, Ketua Adat Tingkat Kecamatan, Kepala \nDesa, dan Tokoh Masyarakat. Pengumpulan data menggunakan teknik observasi, \nwawancara, dan dokumentasi. Analisis data yang digunakan adalah analisis data \ndeskriptif dengan reduksi data, penyajian data. \nHasil penelitian menunjukkan bahwa partisipasi lembaga adat Dayak \nAgabag dalam melestarikan budaya Angalang di wilayah kecamatan Lumbis \nOgong adalah salah satu bentuk keprihatin lembaga adat agar budaya angalang \ntetap dilestarikan dan dikembangkan melalui pelatihan Angalang, pembinaan \nAngalang dan Sosialisasi Budaya Angalang kepada generasi muda agar nilai-nilai \nbudaya seutuhnya terjaga. \nPeneliti menyimpulkan budaya angalang merupakan suatu tradisi adat \nistiadat Dayak Agabag di wilayah kecamatan Lumbis Ogong untuk penyambutan \ntamu Agung, kematian, perkawinan dan budaya angalang diwariskan secara turun \ntemurun sampai saat ini masih dipraktekan dalam kehidupan bermasyarakat.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.061 | 0.011 |
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".