Building Trust in Smart Cities: A Case Study of Seoul Smart City and Recommendations for Calgary's Smart City Alliance
Bibliographic record
Abstract
Big data and technology growth have introduced a compelling opportunity for municipal governments to transform their public service delivery by administering them through Smart Cities. Smart Cities have a crucial role in data-led urban innovation, which can provide the municipality, its civil service, the private sector, and its citizens with improved connectivity, efficiency and overall welfare. All these pillars can strengthen municipal governance, but the broader administration of modern-day technology and big data must be balanced or aware of the concerns produced by such technology to tangibly improve citizen welfare. As these smart developments apply a vast amount of big data, the capstone focused on answering the following question: How are Smart Cities evolving in the context of the technological revolution and how is it that they maintain a relationship between investments in technology and new Smart Cities can maintain a relationship between investments in technology and investments in building trust in technology? Rather than focusing on how much technology is available, the capstone focused on ethical data governance models that build trust in technology to strengthen data fairness, privacy, and transparency. Data-governance processes often depend entirely on the infrastructure ownership models within a city. The research presented literature on a public administration model to set up a case study of a Smart City development that synthesizes the smart city public administration and its data governance capabilities. As there are many avenues to synthesize ethical policymaking in the age of technology, this paper focused on the data collection process. The project employs a case study looking at Seoul, South Korea, where infrastructure ownership rests within public-private partnerships. A case study is an appropriate decision-making tool that can help jurisdictions compare and evaluate policy options to strengthen their public service delivery. The case study used three leading indicators of citizen-centred intelligent cities to address the relationship between technology and the trust built into the technology and public service delivery in Seoul. Data fairness, privacy and transparency, and data governance are indicators.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.023 | 0.010 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".