PROSPEK PRINSIP FIKTIF POSITIF DALAM MENUNJANG KEMUDAHAN BERUSAHA DI INDONESIA
Bibliographic record
Abstract
Prinsip fiktif positif merupakan suatu sarana hukum yang dapat mendukung upaya peningkatan kemudahan berusaha. Tulisan ini akan mendiskusikan lebih lanjut apa sebenarnya prinsip fiktif positif ditinjau dari sudut hukum administrasi, bagaimana peluang dan tantangannya dalam mendukung kemudahan berusaha di Indonesia disamping dalam kerangka perwujudan good governance di Indonesia. Penelitian ini menggunakan pendekatan hukum normatif yang bertumpu kepada penelusuran bahan pustaka atau data sekunder. Dari pengalaman negara-negara lain, penerapan prinsip fiktif positif mampu meminimalisir maladministrasi pelayanan administrasi pemerintahan dan meringkas prosedur hukum yang harus ditempuh dalam pengurusan perizinan untuk memulai dan menjalankan usaha. Dalam konteks Indonesia, konsolidasi hukum dibutuhkan untuk menyesuaikan prinsip fiktif positif dengan berbagai struktur hukum perizinan yang ada, pemaknaan terhadap prinsip fiktif positif harus mampu lebih memperjelas ruang lingkup dan defenisi operasional-normatifnya untuk menghindari bias pemahaman dengan berbagai tindakan hukum administrasi lain yang dapat merugikan warga masyarakat.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.004 |
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".