Investigating the current approach to developing data governance in the Canadian smart city
Bibliographic record
Abstract
Smart cities have grown in prevalence as cities take advantage of big data and connected technologies to address the issues of sustainable urban development in the face of their growing urban populations. Data governance is necessary to smart cities to ensure integrity, accessibility, and accountability of data. There is also a growing concern about having proper data governance to protect citizens’ digital rights and democracy. Though these concerns are pressing, there is a gap in understanding the data governance strategies of city governments and the roles that they play in developing those strategies. Additionally, literature on smart cities often focuses on data privacy and security instead of discussing data governance comprehensively and does not discuss the role of the city. This thesis aims to address this gap by understanding the current state of data governance of proposed Canadian smart cities, through identifying their data governance decisions and classifying them into the roles they are adopting. The Smart Cities Challenge in Canada presented an opportunity to study proposed smart cities for their data governance decisions and the role of the city through content analysis, using concepts from Khatri and Brown’s (2010) data governance framework and Bayat and Kawalek’s (2018) model of data governance city roles. The analysis found that the proposed Canadian smart cities are planning to develop their smart city projects and data governance using an approach driven by open and collaborative principles. This open and collaborative approach adopted by the Canadian smart cities prioritizes data governance activities that address the data access, data principles, and data lifecycle decision domains, in conjunction to the cities taking on roles that emphasize transparency, co-creation, and high stakeholder involvement. Openness and collaboration are discussed to be critical to the success of smart cities, as they can drive mechanisms to help address the challenges of trust and achieve and maintain democratic accountability. This open and collaborative state of smart city data governance also supports a transformation of the smart city discourse, moving away from vendor-driven and citizen-driven smart cities and towards government-driven smart cities. The study outlines considerations for the proposed Canadian smart cities and their stakeholders to act on the gaps in their data governance strategies as identified in the results. Future smart cities are recommended to proactively use an open and collaborative approach in developing their smart city plans and data governance strategies.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".