FAKTOR PENYEBAB BELUM DILAKSANAKANNYA \nPERJANJIAN BAGI HASIL TANAH PERTANIAN BERDASARKAN \nUNDANG-UNDANG NOMOR 2 TAHUN 1960 \nTENTANG PERJANJIAN BAGI HASIL \nDI KECAMATAN ARMA JAYA BENGKULU UTAR
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mendapatkan pengetahuan mengenai \nfaktor penyebab belum dilaksanakannya perjanjian bagi hasil tanah \npertanian berdasarkan Undang-Undang Nomor 2 Tahun 1960 tentang \nPerjanjian Bagi Hasil di Kecamatan Arma Jaya Bengkulu Utara serta untuk \nmendapatkan pengetahuan upaya apa yang dapat dilakukan pemerintah agar \nUndang-Undang tersebut dapat diterapkan di Kecamatan Arma Jaya \nBengkulu Utara. Metode yang digunakan dalam penelitian ini yaitu metode \npenelitian empiris, dalam penelitian ini menggunakan data primer dan data \nsekunder, kemudian dianalisis secara deskriptif kualitatif. Dari hasil \npenelitian ini dapat disimpulkan (1) faktor penyebab belum dilaksanakannya \nperjanjian bagi hasil tanah pertanian berdasarkan Undang-Undang Nomor 2 \nTahun 1960 tentang Perjanjian Bagi Hasil adalah karena karena kurangnya \nperanan penegak hukum dalam memberikan informasi mengenai \npelaksanaan perjanjian bagi hasil tanah pertanian dan juga karena \nmasyarakat masih melaksanakan perjanjian bagi hasil tanah pertanian \nberdasarkan kebiasaannya (2) tidak adanya upaya dari pemerintah agar \nUndang-Undang tersebut dapat diterapkan dan dilaksanakan pada \nmasyarakat di Kecamatan Arma Jaya karena mengenai permasalahan \nmengenai bagi hasil tanah pertanian tidak termasuk ke dalam tugas-tugas \nmereka. \nKata Kunci: perjanjian bagi hasil tanah pertanian
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.186 | 0.098 |
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".