Artificial intelligence and the local government: A five-decade scientometric analysis on the evolution, state-of-the-art, and emerging trends
Why this work is in the frame
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Bibliographic record
Abstract
In recent years, the rapid advancement of artificial intelligence (AI) technologies has significantly impacted various sectors, including public governance at the local level. However, there exists a limited understanding of the overarching narrative surrounding the adoption of AI in local governments and its future. Therefore, this study aims to provide a comprehensive overview of the evolution, current state-of-the-art, and emerging trends in the adoption of AI in local government. A comprehensive scientometric analysis was conducted on a dataset comprising 7112 relevant literature records retrieved from the Scopus database in October 2023, spanning over the last five decades. The study findings revealed the following key insights: (a) exponential technological advancements over the last decades ushered in an era of AI adoption by local governments; (b) the primary purposes of AI adoption in local governments include decision support, automation, prediction, and service delivery; (c) the main areas of AI adoption in local governments encompass planning, analytics, security, surveillance, energy, and modelling; and (d) under-researched but critical research areas include ethics of and public participation in AI adoption in local governments. This study informs research, policy, and practice by offering a comprehensive understanding of the literature on AI applications in local governments, providing valuable insights for stakeholders and decision-makers. • Maps the landscape of artificial intelligence (AI) adoption in local government (LG) • Advancements in the last decades brought the Blooming Era of AI adoption in LG • Main AI adoption purposes: decision support, automation, prediction, service delivery • Main AI adoption areas: planning, analytics, security, surveillance, energy, modelling • Under-researched but critical areas: responsibility and ethics of AI adoption in LG
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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