Social media as communication tools for anti-corruption campaign in Indonesia
Bibliographic record
Abstract
Social media has proven to be quite effective in raising awareness and anti-corruption movements in society. This research aimed to analyze the use of social media Twitter as a means of the Corruption Eradication Commission (KPK) in conducting anti-corruption campaigns in Indonesia. The research employed a qualitative content analysis on the KPK's official Twitter account. The data were processed using the NVIVO 12 Plus software to answer research questions. This research revealed that the KPK's Twitter account is quite active in carrying out anti-corruption campaign activities, although in general it is not optimal. It can be seen from the low intensity of communication and limited communication network so that it is considered as less collaborative. Improving the problems is needed by KPK as it must also show good performance so that public trust continues in high condition. However, this research has limitations in looking at all anti-corruption campaigns carried out by the KPK because it only used Twitter as the reference. Therefore, further research is suggested to analyze all KPK social media such as Youtube and Instagram.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".