Analisis Bibilometrik Perkembangan Strategi Komunikasi di Media Sosial Pada Instansi Pemerintahan Dalam Keamanan Siber
Bibliographic record
Abstract
The Internet has become one of the means for Government Institutions to provide fast and easy services. It also makes the public more actively monitor the progress of public services. The utilization of Social Media by government agencies is an innovation that maximizes technology. Furthermore, the use of the internet through Social Media requires strategies to cope with the advancements of the times as a means of communication. This research utilizes the Scopus database. The article analyzes the characteristics of publications, researchers, universities, and the contributions of countries in the field of Government Institutions conducting communication strategies through social media from 2013-2024 using bibliometric methods. This method is useful because it involves the quantitative analysis of a large number of literatures, using mathematical and statistical methods. The results show that there are 162 documents or articles with the United States, Spain, United Kingdom, China, Canada, Australia, Malaysia, Brazil, Indonesia, and Italy as the countries published in this field. Policy recommendations include the need to enhance the development of Government Institutions to manage their social media in a planned and measurable manner. Further research is expected to focus on public understanding of information provided by government agencies for long-term comprehensibility.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".