The Montreal Model for Integrating Ethics and Legal Compliance to Data Governance Frameworks for Smart City Projects
Bibliographic record
Abstract
This paper explores the concept of the smart city, emphasizing the critical role of data governance and data ethics in ensuring the equitable and responsible use of data. Focusing on Montreal as a case study, the paper examines the city's comprehensive framework for data governance, exemplified by its adoption of the Digital Data Charter. The Digital Data Charter aims to regulate the collection, management, and ethical use of data within urban spaces. To operationalize the principles of the Charter, Montreal has implemented the initiative known as “Le Chantier de la gouvernance des données” (Data Governance Workstream). This program enhances data management practices within organizations, promotes data sharing, and fosters strategic partnerships. Through an analysis of these initiatives, the paper underscores Montreal's commitment to prioritizing ethical considerations and legal compliance in its approach to smart city development. This approach sets a benchmark for other urban centres aiming to leverage digital technologies for the public good.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".