Etika Hakim dalam Kehidupan Sehari-hari: Penggunaan Media Sosial oleh Hakim di Indonesia
Bibliographic record
Abstract
With the development of technology which is rapidly making the community easier in communicating and searching for information such as social media. Social media itself has different characteristics and types and has advantages and disadvantages. The use of social media does not look at a person's status such as age, gender, religion, or profession. At this time many judges use social media to communicate and search for information and interact with the community. Judges in carrying out their obligations as bearers of the legal profession are framed by an institutional institution that is formulated into a code of ethics for the professional profession of judges. Judges within the Supreme Court and lower judicial bodies are bound by the Code of Ethics and the Code of Conduct for Judges (KEPPH) as outlined in the Joint Decree of the Chief Justice and Chair of the Judicial Commission in 2009. Indonesian judges are not prohibited from using social media but the use of social media by judges can raise very important questions because the way judges use social media can influence people's trust in judges in court. In KEPPH there are no rules or ways for judges to use social media. So the judge is unconsciously still bound by KEPPH when using social media. At this time there are still judges who violate KEPPH in using social media. In various countries such as Canada, Rhode Island and UN organizations have made rules and ways for judges to use social media properly and correctly so as not to reduce the public's trust in judges. Therefore, this research was conducted aiming to help the Supreme Court and the Judicial Commission to be able to make regulations or guidelines against judges in using social media, as well as judges to be able to use social media properly and correctly
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".