Analisis Bibilometrik Perkembangan Strategi Komunikasi di Media Sosial Pada Instansi Pemerintahan Dalam Keamanan Siber
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it