#<scp>BlockSidewalk</scp> to Barcelona: Technological sovereignty and the social license to operate smart cities
Bibliographic record
Abstract
Abstract This article explores technological sovereignty as a way to respond to anxieties of control in digital urban contexts, and argues that this may promise a more meaningful social license to operate smart cities. First, we present an overview of smart city developments with a critical focus on corporatization and platform urbanism. We critique Alphabet's Sidewalk Labs development in Toronto, which faces public backlash from the #BlockSidewalk campaign in response to concerns over not just privacy, but also lack of community consultation, the prospect of the city losing its civic ability to self‐govern, and its repossession of public land and infrastructure. Second, we explore what a more responsible smart city could look like, underpinned by technological sovereignty, which is a way to use technologies to promote individual and collective autonomy and empowerment via ownership, control, and self‐governance of data and technologies. To this end, we juxtapose the Sidewalk Labs development in Toronto with the Barcelona Digital City plan. We illustrate the merits (and limits) of technological sovereignty moving toward a fairer and more equitable digital society.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".