Socially sustainable smart cities: mapping the research trends by co-word analysis
Bibliographic record
Abstract
Purpose The article attempts to shed light on the social aspects of research that deal with Sustainable Development Goals (SDGs) and sustainable cities. The aim is to offer a global view of these facets' evolution and to provide information on people-centered smart cities. Design/methodology/approach The research is qualitative. A systematic bibliometric approach is a framework for the research. The unit of analysis is publications on SDGs and Smart Cities (SCs) indexed in Scopus. The authors used VOSviewer text mining functionality to construct co-occurrence networks of socially related critical terms extracted from textual data. The co-occurrence of keywords presents a valuable method and process for attaining in-depth analysis and fast comprehension of trends and linkages in articles from a holistic approach. Findings Social media, social sustainability and social capital are the three multifaceted social keywords that co-occur in SDGs and SCs. The paper provides a brief compendium of resources and frameworks to build a socially sustainable smart city. Research limitations/implications The retrieval date was on 15 August 2019. The authors used the same search query for new papers released in 2019 and afterwards to update their findings. The authors collected 657 documents on SCs, compared to 2,975 documents about SDGs demonstrating that their findings are still trending in the same direction, emphasizing the importance of the research topic. SCs' social aspects are still chartered areas that require the attention to future research. Originality/value The authors’ decision to use two separate data sets for SCs and SDGs data files helps to provide a more comprehensive picture of the research landscape. It may identify areas where research is lacking or needs future research. The authors present an integrative agenda for a smart city to be socially sustainable. Innovative approaches to urban planning are required to empower the place and context and improve the users' satisfaction, where innovative solutions enable smart, sustainable and inclusive societies. Infrastructure governance is a critical keystone. It could guarantee that public investments contribute to sustainable urban development while enhancing city resilience, particularly in facing climate change and inclusive growth challenges.
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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 | 0.013 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.114 | 0.158 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".