USES OF ARTIFICIAL INTELLIGENCE IN THE PUBLIC SECTOR: A READING IN LEADING INTERNATIONAL EXPERIENCES
Bibliographic record
Abstract
The aim of this article is to analyze the most prominent international applications of artificial intelligence in the public sector, as a tool to improve the management structure and improve public services in light of the growing global interest in this technology. The study used a comparative analytical method, and investigated the policies and programs in ten countries (Finland, Estonia, the United Kingdom, the United States, Singapore, Japan, Canada, Australia, Qatar and Saudi Arabia). The results showed that different countries have used different roads and talked about using artificial intelligence in the public sector. For example, Finland and Estonia focused on active services, Canada and Australia, focusing on regulator and governance structure, Japan and Singapore focused on long -term national vision, Qatar and Saudi Arabia rely on major investments and international collaborations, and the UK and the United States focused on the combination of the combination of practitioners. The study indicates the need to achieve an optimal balance between new technologies and moral regulations, at the same time improves long -term strategic plans. This method encourages countries to collaborate and share what they know to speed up digital changes and share the public sector more efficient and long -lasting.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".