Culture of Abuse of Power Due to Conflict of Interest to Corruption for Too Long on The Management form Resources of Oil and Gas in Indonesia
Bibliographic record
Abstract
This research is a culture of abuse of power due to conflicts of interest so that corruption takes too long to manage oil and natural gas resources in Indonesia in managing oil and natural gas resources in Indonesia. The problem of this research is that the Indonesian state is equipped with abundant natural resources, including oil and natural gas resources. According to Article 33 of the 1945 Constitution, the oil and natural gas resources should be controlled by the state for the greatest prosperity of the people. In fact, the Indonesian people are not prosperous despite the abundance of oil and natural gas resources. Historical research methods. With the concept of cultural criminology. The results of research since the independence of the Republic of Indonesia have occurred abuse of power due to conflicts of interest to maintain power in the management of oil and gas with corruption impacting state losses, especially the suffering of the Indonesian people for too long, so that a culture of corruption is formed. This happens first; the sudden change component, caused by global changes and the modernization of the tendency of society to comply with materialism and consumerism while ignoring the cultural values of shame in the life of the nation and state. that is not trustworthy towards people's trust. This renewal of research re-shames cultural morality and limits the extent of power that
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".