Review on HR digitalization and artificial intelligence contributing to smart cities
Bibliographic record
Abstract
This paper aims to identify the research trends on digitalisation contributing to smart cities and the role of technology in economic and social development. Review Methodology is taken up for knowledge development in the area of digital Human Resources Management in line with AI and technology-enabled smart cities. The quality assessment of the review sample is validated through a mixed methods appraisal tool (MMAT). The review conceptualises “Smart Cities” as mainly supply-side and sector-driven, giving the private sector a lead role in problem identification and digital solution facilitating citizens with quick-service delivery. At the heart of digitalisation around smart cities is sustainableefficient- livable urban living. The key terms by peak frequency using Voyant Tools link to “HR Digitalisation” and “Smart City” includes the Internet of Things (IoT), big data analytics, artificial intelligence (AI), advanced energy storage technologies, civic technology, crewless aerial vehicles (drones) and Blockchain as an emerging technology with a substantial presence in smart cities.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".