Prinsip Fair Use terhadap Cover Lagu Musisi Jalanan dalam Perlindungan Hak Cipta
Bibliographic record
Abstract
Cover lagu merupakan kegiatan menyanyikan ulang lagu milik orang lain. \nKegiatan ini telah menjadi trend di kalangan masyarakat salah satunya musisi \njalanan, musisi jalanan memanfaatkan karya musik ini dengan bernyanyi dengan \ntujuan untuk memperoleh keuntungan. Praktik cover lagu yang dilakukan oleh \nmusisi jalanan ini dapat dikategorikan sebagai pelanggaran hak eksklusif pencipta. \nPenerapan prinsip fair use pada cover lagu musisi jalanan masih menjadi isu hukum \nyang kompleks dan memerlukan pemahaman yang lebih baik dari pencipta dan \nmusisi jalanan. Meskipun praktik penggunaan cover lagu musisi jalanan diatur \nsecara jelas dalam Undang-Undang Nomor 28 Tahun 2014 tentang Hak Cipta, \nnamun masih terdapat berbagai tafsiran yang berbeda tentang bagaimana penerapan prinsip fair use. Dengan menggunakan pendekatan perundang-undangan, \nkonseptual dan historis. Penelitian ini dimaksudkan untuk mengkaji penggunaan \nfair use dalam cover lagu musisi jalanan.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads 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".