Trends in the Publication of the Effectiveness and Impact of Digitalization in Population Administration: A Systematic Review
Bibliographic record
Abstract
This research analyzes publication trends and the relationships between research topics in population administration.The five main aspects studied are the distribution of articles per year, publication trends based on country affiliation, publication trends based on institutional affiliation, the number of citations per article, and the relationship between research topics.The results show fluctuations in the number of publications per year, with a peak occurring in 2019, reflecting an increased interest during that period.Indonesia has emerged as a major contributor to publications, highlighting the importance of this topic in the country, particularly concerning the challenges in managing population data.Sepuluh Nopember Institute of Technology (ITS) has become the institution with the most publications, demonstrating a strong focus on this research.Citation analysis shows that older articles tend to have a greater impact, although newer articles may take time to be recognized.The relationship between the research topics shows a close connection between technological innovation, public services, and policy implementation in population administration.In conclusion, although there has been progress, there are still challenges in sustainability and equitable distribution of research, necessitating further collaboration and innovation to enhance impact in this field.
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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 | 0.038 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.018 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".