Culture of Corruption Politicians' Behavior in Parliament and State Official During Reform Government Indonesia (Genealogical Study)
Bibliographic record
Abstract
This study aims to assess and analyze the Culture of Corruption Politicians' Behavior in Parliament and State Official During Reform Government Indonesia (Genealogical Study) This study is genealogical research based on the literature, journals and reporting publications of Indonesian corruption culture. The result of the study concluded that culture corruption behavior of politicians in the parliament and bureaucracy in the reform era in Indonesia is still ongoing corruption can be said as a culture of corruption that has been so severe, that Indonesia is almost categorized as a kleptocracy country, and as a country ruled by thieves (klepto) and even has been spread of viral infections or COVID-19.. This crime could even be called state organized crime in a corrupt government. This crime is based on the achievement of individual interests, groups or political parties and retains the power. The lack of success of Indonesian government in resolving the case of state officials or politicians involved in corruption, collusion and nepotism rapidly lightly court decisions, many cases delayed in its prosecution process, even termination of the case of important officials state to be an indication the weakness of law enforcement against white-collar criminals in Indonesia. This happens due to the severity of conflict of interest so the solution is often based on the interests or political bargaining and abuse of power.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".