Digital Co-Creation in Socially Sustainable Smart City Projects: Lessons From the European Union and Canada
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
Utilizing readily accessible information and communication technologies (ICTs), such as mobile devices, applications, and simple Internet of Things (IoT) sensors, and harnessing their potential through Experimentation as a Service (EaaS), crowdsensing, and gamification, represents one of the most effective approaches to implementing co-creation in smart cities. The benefits of this bottom-up approach are closely related to accurately identifying the real needs of city residents and increasing the chances of designing and implementing solutions with genuine impact, ensuring equity, social inclusion, sustainability, and community resilience. This paper investigates the utilization of ICTs to support social sustainability by analyzing 157 smart city projects funded under the Horizon 2020 program at the European Union level and 5 smart city projects from Canada. The results reveal the utilization of technological solutions such as testbeds, living labs, EaaS, crowdsensing, open data, and more for co-creation in smart city projects. In the discussion part, we point out the importance of focusing on technologies that are familiar to the beneficiaries and on leveraging resources already available as wearable devices or in the citizens’ homes, the versatility of the technological solutions analyzed, the role of heterogeneous and open data, and cross-disciplinary teams in creating new perspectives on urban problems, reducing inequity in the development of solutions to solve them. The concerns raised and problems reported relate to the technology itself (errors in operation), users (difficulties in stimulating their involvement and keeping it constant), and data (quality of data collected, difficult to process, ethics and security of data collection and use). Based on our results, we extract, synthetize and present six distinct categories of lessons learned by the implementation teams of the analyzed projects.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.000 |
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