Review on HR digitalization and artificial intelligence contributing to smart cities
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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