EFEKTIFITAS JAKSA PENGACARA NEGARA DALAM UPAYA PEMULIHAN KEUANGAN NEGARA/DAERAH SEBAGAI AKIBAT TINDAK PIDANA KORUPSI DI KEJAKSAAN NEGERI PURWOKERTO
Bibliographic record
Abstract
<em>Based on the result of the research and a discussion to the main problem that proposed, therefore we can make some conclusion:</em><em> </em><em>The effectivity of State attorney On The Refund of State/Region Monetary Due To Corruption Act In The State Attorney Office Purwokerto, which purpose is to return the loss of state fund or state economy due to corruption act, if we review it from the effectivity of State attorney as one of the state instrument to return the state/region economy </em><em>as a result of corruption act by using UU No 3 Tahun 1971 about </em><em>Eradication of Corruption Act</em><em> </em><em>In The State Attorney Office Purwokerto</em><em>, we can say that it hasn’t effective yet, </em><em>due to Litigation process handled by State attorney which filed civil action to the District Court, no one is capable to pay the loss of state/region due to corruption act in civil ruling, because the defendent already has no possession. Then, on Non Litigation way, using State attorney</em><em> instrument</em><em>, a little bit effective even though has to pay replacement money by credit because the defend</em><em>a</em><em>n</em><em>t</em><em> is capable to pay off the replacement. If we use UU No 31 Tahun 1999 that has been changed and replaced to UU No 20 Tahun 2001 about the Changes </em><em>on UU No 31 Tahun 1999 about Erradication of Corruption Act to a State Attorney, it won’t be a problem because if the defendant is not capable of paying the replacement money based on the decision, the defendant will undergo the subsidiary criminal in the form of penalty, which is the period of time will not exceed the main criminal threat dan the period of time has already decided on the decision.</em> <em>That factors become obstacles, is the State Attorney will not able to perform a sequest sizing because the defendant is unable and has no possession in a nominal of money being corrupted, then from the Law enforcer itself, that the State Attorney is having difficulties to track posession that belongs to the defendant which is gain from the corruption act or assume gained from the corruption act, while the obstacles from the society, there is a lack of awareness from the society itself, which is a lack of concern to give information earlier and detail on the possession belong to the defendant to the law enforcer to a person a suspected as a corruption perpetrators.</em>
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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 teacher head, 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".