Bibliographic record
Abstract
Abstract Privacy is a distinguishing feature of large, cosmopolitan cities whose rising economic and political power calls for new understanding of federalism in the digital age. This chapter explores how the empirically studied link between privacy and big cities translates into a normative commitment to diversity and receives legal expression in varying privacy protections across North-American and European cities. The examples the chapter focuses on are taken from the context of census data, the Equifax credit score, and Facebook data breaches as well as public–private agreements between administrative agencies, and between the public and private sector in the provision of broadband internet, in the shared economy, and in mega smart city projects. The complex picture that emerges from this analysis shows how cities undertake two distinctive roles that might come into conflict: that of privacy activists and of data stewards. As privacy activists, city attorney generals, particularly in the United States, litigate against the state to protect the personal information of vulnerable migrant city dwellers but are also on the forefront of litigation against private companies that might compromise privacy. Publicly spirited, such action on behalf of various cities is not devoid of commercial interest in the face of growing demand for urban infrastructure. As data stewards however, cities might sidestep the public interest altogether—as arguably happened in the case of Toronto Waterfront in Canada and in Barcelona, Spain where rhetoric prevailed over action. This chapter argues that to optimize data privacy but also data sharing in the public interest constitutional recognition for city power should come with the reckoning of the legal mechanism of data trusts as an independent broker between the City and its urban dwellers.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.031 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".