Role of Local Mass Media Control of Regional Governance in Indonesia (Case Study in East Nusa Tenggara)
Bibliographic record
Abstract
Background. Decentralization of the authority to manage government and regional development has been regulated in Law Number 32 of 2014 concerning Regional Government. However, in its implementation there are many deviations found in the form of corruption of local officials causing a loss of quality in human resources. The researcher tried to reveal the role of local mass media to control local governance so that its implementation did not cause corruption in the development budget carried out by regional government officials. Method and material. Using qualitative analysis of news texts published by local mass media with and content of corrupt behavior of regional officials in East Nusa Tenggara Province. Various secondary data is used to supplement this research information. Respondents of journalists and local media editor in chief. Results. Local media coverage revealed 100 regional heads were arrested and convicted of corruption. The impact of the worsening social conditions in the East Nusa Tenggara the high prevalence of stunting is 40.30%; 2,669 infants with malnourished babies, 1,142,790 poor people (21.35%) in 2018. Conclusion. It has been punished by regional officials who are perpetrators of corruption as a result of local media coverage. Corruption has a systemic impact on decreasing the quality of human resources in East Nusa Tenggara.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".