Legal narratives of smart cities: opacity, intelligibility, and compliancy in projects, norms, and futures
Bibliographic record
Abstract
The study focuses on narratives and legal components that form the legal imaginary of smart city projects. This imaginary consists of archetypes of Law and Technology called to engage with prototypes of public policies and to (re)build legal stereotypes for smart city projects. After all, smart cities take core normative claims and goals into their code, in both the technological and legal senses of the term. The public storytelling of smart cities and the public policies of Big Data projects in the municipalities of Rio de Janeiro and Montréal are used as case studies, targeting the respective contexts, risks, and legislations. Three axes composed of six variables are applied for the legal analysis in the case studies: opacity (privacy and security), intelligibility (transparency and participation), and compliance (accountability and governance). Such components are central for the contextualization of technological issues of smart cities under a narrative that is more accessible by law, the identification of specific concerns that present social risks to the rule of law, and the justification of measures required by legislation to protect fundamental rights. Brazilian and Canadian legislative references are used as hypothetical exercises on the legal frameworks related to the in-house Big Data projects. Normative sources and elements for further research can be found in each analysis.
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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 | 0.001 | 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 | 0.001 |
| 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 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".