Using Artificial Intelligence in Public Management: Aspects of Integration
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 article examines various aspects of the use of artificial intelligence in the public administration system. In particular, the world experience of implementing intelligent technologies in management activities of the USA, China, Singapore, Japan, UAE, India, UK, Canada, Germany as leaders in the implementation of effective digital innovations in the field of public administration is considered. Attention is focused on creating favorable conditions at the state level to support initiatives for the development of artificial intelligence and determining its exceptional role in the further development of society. An analysis of Ukrainian practices of integrating artificial intelligence technologies into public administration proves that the use of digital innovations in domestic management activities is a definite pointer for the introduction and approval of relevant social, political, legal, economic, cultural norms in order to form a modern digital society, the most important element of which there is the development and active integration of artificial intelligence technologies into the management system and the formation of a «new» netocratic public administration. The article highlights key aspects of the implementation of artificial intelligence in public administration, including automation of administrative processes, data analysis, open access and exchange of information, security and protection of information, flexibility and adaptability, ensuring public participation, electronic platforms for citizen participation, identification and counteraction corruption, hybrid service systems, efficiency and innovation.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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