Framing Analysis of Village Funding Corruption in Media Suaramerdeka.Com in Central Java, Indonesia, 2019
Bibliographic record
Abstract
Various corruption cases have occurred in many villages in Central Java, Indonesia. Corruption is mostly carried out by village officials in running the government. One of many factors causing such corruption cases is the existence of village fund assistance. These corruption cases have been published in the mass media, Specifically on online media, i.e. suaramerdeka.com This study aims to look at the media framing of corruption cases that have occurred in Central Java, particularly concerning village funding assistance in various regions through the online media suaramerdeka.com. This study is a constructive paradigm research. The approach method used is qualitative. Meanwhile, the analytical method used is the Robert N. Entman framing analysis model. The findings of this study stated that suaramerdeka.com framed the perpetrators of corruption as village officials. Although it is acknowledged that not all of them are corrupt, the news seems to give the impression that village officials have conducted a lot of corruption. However, on the other hand, the news content of suaramerdeka.com as a local media in Central Java also lacks detail in reporting corruption issues. Moreover, it seems only normative and does not show that it is a strong media in covering corruption cases. The explanation of the news was not very in-depth.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".