Corruption Eradication in Indonesia during the Covid-19 Pandemic: An Analysis of the Implementation of Article 27 Law Number 2 of 2020 Concerning State Financial Policy and Financial System Stability for Handling Covid-19 Pandemic
Bibliographic record
Abstract
As Indonesia announced its first Covid-19 case on 2 March 2020, the government issued Acts Number 2 Year 2020. Article 27.1 and 27.2 of the Act do not provide legal certainty because they may release the state-official-corruptors from their criminal responsibility. Through this paper, the author argues the criminal-responsibility exception by elaborating the ideas of the 1945 Constitution and the Corruption Act. The author uses normative legal research to construct the paper by bringing the 1945 Constitution, Indonesian Penal Code, and Government Administration Act as contra-materials toward Acts Number 2 Year 2020. The author also uses the theories from Indonesian Law Scholars to base the author’s argument. The paper provides the construction of criminal corruption as one of the essential parts of state loss. It also explains the solution to remove the criminal-responsibility-exception by using the excellent faith principle. The paper would return the good faith principle into the implementation of Act Number 2 Year 2020. As Act Number 2 Year 2020 is considerably new on implementation, this paper provides new insight into the better implementation of corruption-handling during the Covid-19 Pandemic.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".